diff --git a/GC_content.R b/GC_content.R
new file mode 100644
index 0000000000000000000000000000000000000000..6db2d9728f4e171faf3b231853098abc9da31e75
--- /dev/null
+++ b/GC_content.R
@@ -0,0 +1,66 @@
+# GC content and CpG % plots 
+
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(dplyr)
+
+
+promoter <- fread("dunnart_promoters_homerAnnot.txt")
+enhancer <- fread("dunnart_enhancers_homerAnnot.txt")
+peaks = list(promoter, enhancer)
+peaks = Map(mutate,peaks,cre=c("promoters", "enhancers"))
+peaks = lapply(peaks, function(x) x %>% dplyr::select("Chr", "Start", "End", "Peak Score", "Distance to TSS", "CpG%", "GC%", "cre") %>% as.data.table())
+peaks = rbindlist(peaks)
+colnames(peaks)[5:7] <- c("distanceToTSS","CpG", "GC")
+peaks[is.na(peaks)] <- 0
+peaks = peaks[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
+promoter = subset(peaks, cre == "promoters")
+enhancer = subset(peaks, cre == "enhancers")
+promoter.subset=subset(promoter, log10_abs_dist >= 3.5)
+promoter.subset2 = subset(promoter, log10_abs_dist <3.5)
+promoter.subset$cre = "subset>3.5"
+promoter.subset2$cre = "subset<3.5"
+data = rbind(enhancer, promoter, promoter.subset2, promoter.subset)
+
+p <- ggplot(data, aes(factor(cre), y = GC)) + 
+  #geom_violin(aes(fill=factor(cre), color=factor(cre)),
+  #  position = "dodge"
+  #  )+
+  geom_boxplot(aes(color=factor(cre)),
+    #outlier.shape = NA,
+    width = .2, 
+    notch = TRUE,
+    fill=c("#FCF8EC","#E4F3F1", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("CpG %") 
+pdf("GC.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F","#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9","#E9C46A","#2A9D8F"))
+dev.off()
+
+pairwise.wilcox.test(data$GC, data$cre, p.adjust.method="fdr")
+
+p <- ggplot(peaks, aes(factor(cre), y = GC.width)) + 
+  geom_violin(aes(fill=factor(cre), color=factor(cre)),
+    position = "dodge"
+    )+
+  geom_boxplot(aes(color=factor(cre)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("GC content %") 
+pdf("GC.width_plot.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" )
+dev.off()
\ No newline at end of file
diff --git a/GO_orthologous_peaks.R b/GO_orthologous_peaks.R
new file mode 100644
index 0000000000000000000000000000000000000000..5d3b7c9a504846d9db1f4e241471ae825d2bea2f
--- /dev/null
+++ b/GO_orthologous_peaks.R
@@ -0,0 +1,151 @@
+## GO analysis on orthologous peaks in mouse and dunnart
+## Input is liftOver peaks in mouse coordinates from comparePeaks.R
+
+## Load packages
+library(clusterProfiler)
+library(data.table)
+library(enrichplot)
+library(GOSemSim)
+library(ChIPseeker)
+library(TxDb.Mmusculus.UCSC.mm10.knownGene)
+library(org.Mm.eg.db)
+library(viridis)
+library(ComplexHeatmap)
+library(tidyverse)
+library(ggplot2)
+library(plyr)
+library(UpSetR)
+txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene
+mmGO = godata('org.Mm.eg.db', ont="BP") 
+
+###################################################################################################
+############################### GENE ONTOLOGY & PATHWAY ANALYSIS ##################################
+###################################################################################################
+
+compareOrthoPeaks <- function(bedList, dotplot, names, upsetPlot, mergedNames){
+    bedFiles = list.files(bedList, pattern= "liftOver.bed", full.names=T) # create list of files in directory
+    bedFiles = as.list(bedFiles)
+
+    ## Annotate peaks
+    peakAnnoList <- lapply(bedFiles, annotatePeak, TxDb=txdb,
+                       tssRegion=c(-3000, 3000), verbose=FALSE, annoDb="org.Mm.eg.db")
+    
+    ## Make dataframe from upset plot
+    df2 = lapply(peakAnnoList, function(x) as.data.frame(x@anno))
+    subset = lapply(df2, function(x) x %>% dplyr::select(ENSEMBL) %>% as.data.table() %>% unique())
+    names(subset) = names
+    subset = Map(mutate, subset, stage = names(subset)) 
+    merged <- subset %>% purrr::reduce(full_join, by = "ENSEMBL")  # join all dataframes by ensembl geneID
+    merged[is.na(merged)] <- 0
+    merged = as.data.frame(merged)
+    geneId = merged$geneId
+    colnames(merged) = mergedNames 
+    upset.df = data.frame(lapply(merged[,2:8], function(x) as.numeric(x!="0")))
+    rownames(upset.df) = geneId
+
+    ## Upset plot
+    pdf(upsetPlot)
+    print(upset(upset.df, 
+    nsets=7, order.by="freq", 
+    number.angles = 45))
+    dev.off()
+
+    ## Make a background universe for GO enrichment testing
+    ## Take the union of ALL genes with peaks for mouse and dunnart
+    bedFiles = list.files(backgroundList, pattern= ".txt", full.names=T) # create list of files in directory
+    bedFiles = as.list(bedFiles)
+    data = lapply(bedFiles, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA")))
+    data = lapply(data, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
+    genes = lapply(data, function(i) as.data.frame(i)$mouseensembl)
+    genes = lapply(genes, function(x) bitr(x, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db"))
+    bkg_genes = lapply(genes, function(i) as.data.frame(i)$ENTREZID)
+    bkg_genes_union = Reduce(union, genes) 
+
+    ## GO terms 
+    genes = lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
+    names(genes) = names
+    go_cluster <- pairwise_termsim(
+      setReadable(
+      compareCluster(
+        geneCluster = genes,
+        fun = enrichGO, 
+        ont="BP",
+        keyType="ENTREZID", 
+        pAdjustMethod = "fdr",
+        pvalueCutoff = 0.001, 
+        OrgDb = org.Mm.eg.db),
+      OrgDb = org.Mm.eg.db, 
+      keyType="ENTREZID"))
+
+    ## Dotplot
+    pdf(dotplot, width = 20, height = 10)
+    print(dotplot(go_cluster) + 
+    scale_color_viridis() + 
+    theme(axis.text.x = element_text(angle = 45, hjust=1)))
+    dev.off()
+
+    return(genes)
+}
+
+names = c("E10", "E11", "E12", "E13", "E14", "E15", "dunnart")
+
+all_promoters = compareOrthoPeaks(bedList = "GO_analysis/promoters", backgroundList = "../consensus/promoters/clustered", names = c("E10_mouse", "E10_combined", "E10_dunnart", "E11_mouse", "E11_combined", "E11_dunnart",
+    "E12_mouse", "E12_combined", "E12_dunnart", "E13_mouse", "E13_combined", "E13_dunnart", 
+    "E14_mouse", "E14_combined", "E14_dunnart", "E15_mouse", "E15_combined", "E15_dunnart"), mergedNames = c("geneId", "E10_mouse", "E10_combined", "E10_dunnart", "E11_mouse", "E11_combined", "E11_dunnart",
+    "E12_mouse", "E12_combined", "E12_dunnart", "E13_mouse", "E13_combined", "E13_dunnart", 
+    "E14_mouse", "E14_combined", "E14_dunnart", "E15_mouse", "E15_combined", "E15_dunnart"), dotplot = "promoter_dotplot_GOenrich.pdf", upsetPlot = "promoter_upsetPlot_GOenrich.pdf")
+
+all.enhancers = compareOrthoPeaks(bedList = "GO_analysis/enhancers", names = c("E10_dunnart", "E10_mouse", "E10_combined", "E11_dunnart","E11_mouse", "E11_combined", 
+     "E12_dunnart","E12_mouse", "E12_combined",  "E13_dunnart","E13_mouse", "E13_combined", "E14_dunnart", 
+    "E14_mouse", "E14_combined",  "E15_dunnart", "E15_mouse","E15_combined"), mergedNames = c("geneId", "E10_dunnart", "E10_mouse", "E10_combined", "E11_dunnart","E11_mouse", "E11_combined", 
+     "E12_dunnart","E12_mouse", "E12_combined",  "E13_dunnart","E13_mouse", "E13_combined", "E14_dunnart", 
+    "E14_mouse", "E14_combined",  "E15_dunnart", "E15_mouse","E15_combined"), dotplot = "enhancer_dotplot_GOenrich.pdf", upsetPlot = "enhancer_upsetPlot_GOenrich.pdf")
+
+enhancerSim = mclusterSim(all.enhancers, semData=mmGO, measure="Wang", combine="BMA")
+pdf("enhancerGOSemSim_heatmap.pdf")
+Heatmap(enhancerSim)
+dev.off()
+
+promoterSim = mclusterSim(all.promoters, semData=mmGO, measure="Wang", combine="BMA")
+pdf("promoterGOSemSim_heatmap.pdf")
+Heatmap(promoterSim)
+dev.off()
+
+## Now look at merged mouse peaks across all stages and compare to the dunnart
+
+## 
+bedFiles = list("GO_analysis/merged/dunnart_specific_mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra_smiCraTOmm10.bed", "GO_analysis/merged/mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra_smiCraTOmm10.bed", "GO_analysis/merged/mouse_specific_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10.bed")
+bedFiles = list("GO_analysis/merged/dunnart_specific_mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra_smiCraTOmm10.bed", "GO_analysis/merged/mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra_smiCraTOmm10.bed", "GO_analysis/merged/mouse_specific_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10.bed")
+
+peakAnnoList <- lapply(bedFiles, annotatePeak, TxDb=txdb,
+                       tssRegion=c(-3000, 3000), verbose=FALSE, annoDb="org.Mm.eg.db")
+
+genes = lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
+names(genes) = c("dunnart_biased", "common", "mouse_biased")
+go_cluster <- simplify(
+      setReadable(
+      compareCluster(
+        geneCluster = genes, 
+        fun = enrichGO, 
+        ont="BP", 
+        keyType="ENTREZID", 
+        pvalueCutoff = 0.01, 
+        OrgDb = org.Mm.eg.db),
+      OrgDb = org.Mm.eg.db, 
+      keyType="ENTREZID"),
+      cutoff=0.9, 
+      by="p.adjust", 
+      select_fun=min)
+
+pdf("summary_enhancer_cnetplot_GOenrich.pdf")
+print(cnetplot(go_cluster))
+dev.off()
+
+pdf("summary_enhancer_dotplot_GOenrich.pdf", width = 10, height = 8)
+print(dotplot(go_cluster, showCategory = 15) + theme(axis.text.x = element_text(angle = 45, hjust=1)))
+dev.off()
+
+
+
+
+
diff --git a/GO_plots.R b/GO_plots.R
new file mode 100755
index 0000000000000000000000000000000000000000..d91818e4ba7f98c6e479e8a90ee31296e8fb6745
--- /dev/null
+++ b/GO_plots.R
@@ -0,0 +1,83 @@
+## Script that takes as input file with GO enriched terms and returns you a barplot
+library(dplyr); library(data.table)
+library(magrittr); 
+library(ggthemes);library(ggplot2);library(ggpubr)
+
+## change here the working directory(ies)
+setwd('/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/4-dunnartPeaks/1-FullDataset/')
+
+go_files_dir='/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters/GO_noOverlap/'
+plot_dir='/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters/GO_noOverlap'
+
+## read files into the directory
+## it creates a list of dataframes (one per file)
+## then discards the geneID field 
+
+read_files=function(dir){
+  x=as.character(list.files(dir,recursive =F,full.names = T)) %>% 
+    lapply(function(y)
+      fread(y,sep = '\t',header = T)[
+        ,geneID:=NULL
+      ] %>% setorderv('p.adjust',1)) ## sorted by most significant ones 
+  ## this returns you the filename without file extension. 
+  ## I will use it as plot title below (you can remove it if u want)
+  file_names=as.character(list.files(dir,recursive =F,full.names = F))
+  file_names=gsub("\\..*","",file_names) 
+  names(x)=file_names
+  ## I will make a new field with the filename that I will use as categorical variable to assign different colors
+  ## i.e. each plot will have a different color
+  x=Map(mutate,x,filename=names(x))
+  x=lapply(x,function(y)y=as.data.table(y))
+  return(x)
+}
+
+go_terms=read_files(go_files_dir)
+
+## I will make another column with the color for the given file. 
+## all row entries will be identical but I just need this for plotting
+## I am going to do in this way so that u can change the colors as u like for each file
+
+colors=c('#6699CC','#6699CC','#6699CC','#6699CC','#6699CC','#6699CC','#6699CC','#6699CC',
+'#6699CC','#6699CC','#6699CC','#6699CC','#6699CC','#6699CC') ## remember to add a color per input file
+
+go_colors=Map(cbind, go_terms, color=colors)
+go_colors=lapply(go_colors, function(x)x=as.data.table(x))
+
+## this keeps only the significant GO terms
+significant_go_terms=lapply(go_colors,function(x)x=x[p.adjust<=0.05])
+
+plot_results=function(file){
+  
+  top_n_GOs=copy(file)
+  top_n_GOs=top_n_GOs[1:20]
+  
+  ggplot(top_n_GOs, 
+         aes(x=reorder(Description,-log10(p.adjust)), y=-log10(p.adjust),fill=filename)) + ## this reorders the x-axis and converts the p-adj to log10 values
+    geom_bar(stat = 'identity',position = 'dodge')+
+    xlab(" ") +
+    ylab("\n -Log10 (P) \n ") +
+    scale_y_continuous(breaks = round(seq(0, max(-log10(top_n_GOs$p.adjust)), by = 2), 1)) +
+    scale_fill_manual(name= " ",values=top_n_GOs$color)+
+    theme( ## these are just parameters to change the theme of the plot. you can add more or remove them
+      legend.position='none',
+      legend.background=element_rect(),
+      axis.text.x=element_text(angle=0, hjust=1.10),
+      axis.text.y=element_text(angle=0, vjust=0.8),
+      axis.title=element_text(),
+      axis.line = element_line(color = "black",size = 0, linetype = "solid"),
+      panel.background =element_rect(fill = 'white', size = 0.5,colour = 'black'),
+      panel.grid.minor = element_blank(),
+      panel.grid.major = element_blank(),
+      title=element_text()) +
+    coord_flip()
+  
+}
+
+go_plot=lapply(significant_go_terms,function(x)x=plot_results(x))
+
+
+# Save each plot to a different pdf in the plot_dir/
+lapply(names(go_plot), 
+       function(x)ggsave(filename=paste(plot_dir,x,'_plot',".pdf",sep=""), 
+                         plot=go_plot[[x]]))
+
diff --git a/GO_similarity_PCA.R b/GO_similarity_PCA.R
new file mode 100644
index 0000000000000000000000000000000000000000..313e163c65557398708cc8aaaf1f5daa1c61ebb0
--- /dev/null
+++ b/GO_similarity_PCA.R
@@ -0,0 +1,101 @@
+## input: vector of GO terms
+## output: heatmap semantic similarity scores
+library(tools)
+library(GOSemSim)
+library(ComplexHeatmap)
+library(circlize)
+library('BiocParallel')  
+library(data.table)
+library(tidyverse)
+
+## GO data from mouse org database for biological processes
+## Can do for mouse phenotype and molecular processes
+mmGO = godata('org.Mm.eg.db', ont="BP") 
+plot_dir = ("consensus/promoters/")
+## Wang is actually a guy but here it refers to a semantic similarity metric (see https://yulab-smu.top/biomedical-knowledge-mining-book/semantic-similarity-overview.html)
+## combine = 'BMA' returns a single value estimating how similar the 2 sets are, you can change it with min,max,avg ... (see link above)
+
+goPCA <- function(fileList, file_to_compare_to){
+    files =list.files(fileList, pattern= "", full.names=T) # create list of files in directory
+    files = as.list(files)
+    list = lapply(files, function(x) read.table(x, header=TRUE, sep="\t", quote = "") %>% as.data.table()) # read in all files
+    ## this keeps only the significant GO terms
+    significant_go_terms=lapply(list,function(x) x=x[p.adjust<=0.01])
+    listVector = lapply(significant_go_terms, function(x) dplyr::pull(x, ID))
+    file_input = read.table(file_to_compare_to, sep="\t", header=TRUE, quote="") %>% as.data.frame()
+    file_sig_go = dplyr::pull(file_input[file_input$p.adjust <= 0.01,], ID)
+
+    ## This extracts the file names for all the files in the list (all embryonic stages)
+    file_names=as.character(list.files(fileList,recursive =F,full.names = F)) ## directory with all embryonic stages
+    file_names=gsub("\\..*","",file_names) ## Removes everything except 'E*.5'
+    file_names=gsub("[^0-9]","", file_names) ## Removes the 'E' so that it's a number
+    names(listVector)=file_names ## Add new names to list
+
+    ## Check if input file name is less than or equal to any in listVector
+    ## This will avoid running comparisons that have already been run
+    ## Eg. E10.5 vs E11.5 is the same as E11.5 vs E10.5
+    file_to_compare_to_name = basename(file_path_sans_ext(file_to_compare_to)) ## Name of file to compare to
+    if(grepl('^E', file_to_compare_to_name) == TRUE){ ## If file begins with E (this is so that it doesn't stall with the dunnart files)
+        file_to_compare_to_name=gsub("\\..*","",file_to_compare_to_name) ## Removes everything except 'E*.5'
+        file_to_compare_to_name=gsub("[^0-9]","",file_to_compare_to_name) ## Removes the 'E' so that it's a number
+        listVectorSubset = listVector[names(listVector) > file_to_compare_to_name] ## Keep only vectors in the list that are greater than the file being compared to
+                                                                                ## Eg. this means that when comparing E12 to the list then it will only compare to 13,14 & 15
+    } else 
+    listVectorSubset = listVector
+
+    ## Compare the similarity between GO lists
+    ## This will parallelise the lapply function 
+    comparisons = bplapply(listVectorSubset,function(x) mgoSim(file_sig_go,x, semData=mmGO,measure="Wang", combine='NULL')) 
+    return(comparisons)
+}
+
+## This command checks the time it takes to run
+## One comparison is about 95 seconds
+system.time(mgoSim(listVector[[1]], listVector[[2]],semData=mmGO, measure="Wang", combine='BMA'))
+
+register(SerialParam())
+
+
+## Promoters
+plot_dir = ("consensus/promoters/")
+
+dunnartvsmouse = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/dunnart_GO/promoter_mm10GOenrich") #dunnart versus all mouse stages
+
+e10vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E10.5_promoter_mm10GOenrich") 
+
+e11vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E11.5_promoter_mm10GOenrich")
+
+e12vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E12.5_promoter_mm10GOenrich") 
+
+e13vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E13.5_promoter_mm10GOenrich") 
+
+e14vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E14.5_promoter_mm10GOenrich") 
+
+
+## Enhancers
+plot_dir = ("consensus/enhancers/")
+
+dunnartvsmouse = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/dunnart_GO/enhancer_mm10GOenrich_subsample.txt")
+
+e10vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E10.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e11vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E11.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e12vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E12.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e13vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E13.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e14vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E14.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
diff --git a/README.md b/README.md
old mode 100644
new mode 100755
diff --git a/annotation_10M.R b/annotation_10M.R
new file mode 100755
index 0000000000000000000000000000000000000000..fea1ee3857b096e08a84af4bf747bd2a28879626
--- /dev/null
+++ b/annotation_10M.R
@@ -0,0 +1,338 @@
+library(GenomicFeatures)
+library(rtracklayer)
+library(ChIPseeker)
+library(clusterProfiler)
+library(org.Mm.eg.db)
+library(ChIPpeakAnno)
+library(tidyverse)
+library(RColorBrewer)
+library(ggplot2)
+library(data.table)
+library(dplyr)
+library(TxDb.Mmusculus.UCSC.mm10.ensGene)
+###################################################################################################
+################################### PEAK ANNOTATION ###############################################
+###################################################################################################
+
+## Make txdb for dunnart annotation file
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+smiCra_txdb <- makeTxDbFromGFF("Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff")
+
+
+## geneID conversion tables
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+df2 <- read.table("biomart_ensembl.txt", header=TRUE, sep="\t")
+df3 <- read.table("bioDBnet_refseq2ensembl_H3K27ac.txt", header=TRUE, sep="\t")
+
+
+#################################################### DUNNART ANNOTATION ##########################################################
+
+## Anotate peak files
+annotatePeaks <- function(peak, outFile, outFile1, annoBar, distanceTSS, heatmapTSS, GOenrich, kegg){
+  
+  # Annotate peak file based on dunnart GFF
+  peakAnno <- annotatePeak(peak, tssRegion = c(-3000, 3000), TxDb = smiCra_txdb)
+  
+  # Plot annotation
+  # pdf(file=annoBar, width=10, height=7)
+  # print(plotAnnoBar(peakAnno))
+  # dev.off()
+  
+  # Set promoter range
+  # promoter <- getPromoters(TxDb=smiCra_txdb, upstream=3000, downstream=3000)
+  
+  # Find distance from TSS
+  # tagMatrix <- getTagMatrix(toGRanges(peak), windows=promoter)
+  
+  # Plot heatmap on peak distance to TSS
+  # pdf(file=heatmapTSS, width=10, height=7)
+  # print(tagHeatmap(tagMatrix, xlim=c(-3000, 3000), color = "#6699CC"))
+  # dev.off()
+
+  # Plot distance from TSS
+  # pdf(file=distanceTSS, width=10, height=7)
+  # print(plotAvgProf(tagMatrix, xlim=c(-3000, 3000)))
+  # dev.off()
+
+  # Write annotation to file
+  write.table(peakAnno, outFile, sep="\t", quote=F, row.names=F)
+  
+  peakAnnoDF <- as.data.frame(peakAnno, row.names = NULL)
+  # Convert refseq IDs and geneIDs to devil ensembl IDs
+  peakAnnoDF$ensemblgeneID <- df2$Gene.stable.ID[match(unlist(peakAnnoDF$geneId), df2$Gene.name)]
+  peakAnnoDF$transcriptIdAltered <- gsub("\\..*","", peakAnnoDF$transcriptId)
+  peakAnnoDF$refseqID <- df3$Ensembl.Gene.ID[match(unlist(peakAnnoDF$transcriptIdAltered), df3$RefSeq.mRNA.Accession)]
+  peakAnnoDF$combined <- ifelse(peakAnnoDF$refseqID == "-", peakAnnoDF$ensemblgeneID, peakAnnoDF$refseqID)
+  peakAnnoDF$refseqID[peakAnnoDF$refseqID == as.character("-")] <- NA
+  
+  # Convert devil ensembl to mouse ensembl
+  peakAnnoDF$mouseensembl <- df2$Mouse.gene.stable.ID[match(unlist(peakAnnoDF$combined), df2$Gene.stable.ID)]
+  # Write annotation with converted IDs 
+  peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$mouseensembl),]
+  write.table(peakAnnoDF, outFile1, sep="\t", quote=F, row.names=F)
+
+  # GO enrichment analysis
+  GO <- enrichGO(gene = peakAnnoDF$mouseensembl,
+                 keyType = "ENSEMBL",
+                 OrgDb = org.Mm.eg.db,
+                 ont = "BP",
+                 pAdjustMethod = "BH",
+                 qvalueCutoff = 0.05,
+                 readable = TRUE)
+  write.table(GO, GOenrich, sep="\t", quote=F, row.names=F)
+  
+  ## convert mouse ensembl to entrezID
+  suppressWarnings(gene.df <- bitr(geneID=peakAnnoDF$mouseensembl, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" ))
+  
+  ## kegg analysis
+  kk <- enrichKEGG(gene = gene.df$ENTREZID, organism = "mmu", pvalueCutoff=0.05)
+  write.table(kk, kegg, sep="\t", quote=F, row.names=F)
+}
+
+
+### Consensus peaks
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers/")
+annotatePeaks(peak = "dunnart_enhancer_peaks_subsample.narrowPeak", outFile = "dunnart_enhancer_annotation_subsample.txt", annoBar = "dunnart_enhancer_annoBar_subsample.pdf", distanceTSS = "dunnart_enhancer_distanceTSS_subsample.pdf", heatmapTSS = "dunnart_enhancer_heatmapTSS_subsample.pdf", outFile1 = "dunnart_enhancer_annotationConvertedIDs_subsample.txt", GOenrich = "enhancer_mm10GOenrich_subsample.txt", kegg="enhancer_KEGG_subsample.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/clustered")
+annotatePeaks(peak = "dunnart_promoter_peaks_subsample.narrowPeak", outFile = "dunnart_promoter_annotation_subsample.txt", annoBar = "dunnart_promoter_annoBar_subsample.pdf", distanceTSS = "dunnart_promoter_distanceTSS_subsample.pdf", heatmapTSS = "dunnart_promoter_heatmapTSS_subsample.pdf", outFile1 = "dunnart_promoter_annotationConvertedIDs_subsample.txt" , GOenrich = "promoter_mm10GOenrich_subsample.txt", kegg="promoter_KEGG_subsample.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/H3K4me3/")
+annotatePeaks(peak = "H3K4me3_overlap_default.narrowPeak", outFile = "H3K4me3_overlap_annotation.txt", annoBar = "H3K4me3_overlap_annoBar.pdf", distanceTSS = "H3K4me3_overlap_distanceTSS.pdf", heatmapTSS = "H3K4me3_overlap_heatmapTSS.pdf", outFile1 = "H3K4me3_overlap_annotationConvertedIDs.txt", GOenrich = "H3K4me3_overlap_mm10GOenrich", kegg="H3K4me3_overlap_KEGG.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/H3K27ac/")
+annotatePeaks(peak = "H3K27ac_overlap_default.narrowPeak", outFile = "H3K27ac_overlap_annotation.txt", annoBar = "H3K27ac_overlap_annoBar.pdf", distanceTSS = "H3K27ac_overlap_distanceTSS.pdf", heatmapTSS = "H3K27ac_overlap_heatmapTSS.pdf", outFile1 = "H3K27ac_overlap_annotationConvertedIDs.txt" , GOenrich = "H3K27ac_overlap_mm10GOenrich", kegg="H3K27ac_overlap_KEGG.txt")
+
+annotatePeaks(peak = "consensus/promoters/dunnart.clustered.narrowPeak", outFile = "cluster1_promoter_annotation.txt", annoBar = "cluster1_promoter_annoBar.pdf", distanceTSS = "cluster1_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_promoter_heatmapTSS.pdf", outFile1 = "cluster1_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster1_mm10GOenrich.txt", kegg="cluster1_KEGG.txt")
+annotatePeaks(peak = "consensus/promoters/cluster2_promoters.narrowPeak", outFile = "cluster2_promoter_annotation.txt", annoBar = "cluster2_promoter_annoBar.pdf", distanceTSS = "cluster2_promoter_distanceTSS.pdf", heatmapTSS = "cluster2_promoter_heatmapTSS.pdf", outFile1 = "cluster2_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster2_mm10GOenrich.txt", kegg="cluster2_KEGG.txt")
+annotatePeaks(peak = "consensus/promoters/cluster3_promoters.narrowPeak", outFile = "cluster3_promoter_annotation.txt", annoBar = "cluster3_promoter_annoBar.pdf", distanceTSS = "cluster3_promoter_distanceTSS.pdf", heatmapTSS = "cluster3_promoter_heatmapTSS.pdf", outFile1 = "cluster3_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster3_mm10GOenrich.txt", kegg="cluster3_KEGG.txt")
+
+
+### Replicates
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/replicates/H3K4me3/")
+annotatePeaks(peak = "A-2_H3K4me3_vs_A-1_input_macs2_default_peaks.narrowPeak", annoBar = "A-2_H3K4me3_annoBar.pdf", distanceTSS = "A-2_H3K4me3_distanceTSS.pdf", heatmapTSS = "A-2_H3K4me3_heatmapTSS.pdf", outFile = "A-2_H3K4me3_annotation.txt",  outFile1 = "A-2_H3K4me3_annotationConvertedIDs.txt", GOenrich = "A-2_H3K4me3_mm10GOenrich", kegg="A-2_H3K4me3_KEGG.txt")
+annotatePeaks(peak = "B-2_H3K4me3_vs_B-1_input_macs2_default_peaks.narrowPeak", annoBar = "B-2_H3K4me3_annoBar.pdf", distanceTSS = "B-2_H3K4me3_distanceTSS.pdf", heatmapTSS = "B-2_H3K4me3_heatmapTSS.pdf", outFile = "B-2_H3K4me3_annotation.txt",  outFile1 = "B-2_H3K4me3_annotationConvertedIDs.txt", GOenrich = "B-2_H3K4me3_mm10GOenrich", kegg="B-2_H3K4me3_KEGG.txt")
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/replicates/H3K27ac/")
+annotatePeaks(peak = "A-3_H3K27ac_vs_A-1_input_macs2_default_peaks.narrowPeak", annoBar = "A-3_H3K27ac_annoBar.pdf", distanceTSS = "A-3_H3K27ac_distanceTSS.pdf", heatmapTSS = "A-3_H3K27ac_heatmapTSS.pdf", outFile = "A-3_H3K27ac_annotation.txt",  outFile1 = "A-3_H3K27ac_annotationConvertedIDs.txt", GOenrich = "A-3_H3K27ac_mm10GOenrich", kegg="A-3_H3K27ac_KEGG.txt")
+annotatePeaks(peak = "B-3_H3K27ac_vs_B-1_input_macs2_default_peaks.narrowPeak", annoBar = "B-3_H3K27ac_annoBar.pdf", distanceTSS = "B-3_H3K27ac_distanceTSS.pdf", heatmapTSS = "B-3_H3K27ac_heatmapTSS.pdf", outFile = "B-3_H3K27ac_annotation.txt",  outFile1 = "B-3_H3K27ac_annotationConvertedIDs.txt", GOenrich = "B-3_H3K27ac_mm10GOenrich", kegg="B-3_H3K27ac_KEGG.txt")
+
+## Overlaps
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/enhancers")
+annotatePeaks(peak = "enhancer_summit100bp_mm10TOsmiCra1.bed", outFile = "enhancer_summit100bp_annotation.txt", annoBar = "enhancer_summit100bp_annoBar.pdf", distanceTSS = "enhancer_summit100bp_distanceTSS.pdf", heatmapTSS = "enhancer_summit100bp_heatmapTSS.pdf", outFile1 = "enhancer_summit100bp_annotationConvertedIDs.txt" , GOenrich = "enhancer_summit100bp_mm10GOenrich", kegg="enhancer_summit100bp_KEGG.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters")
+annotatePeaks(peak = "promoter_summit100bp_mm10TOsmiCra1.bed", outFile = "promoter_summit100bp_annotation.txt", annoBar = "promoter_summit100bp_annoBar.pdf", distanceTSS = "promoter_summit100bp_distanceTSS.pdf", heatmapTSS = "promoter_summit100bp_heatmapTSS.pdf", outFile1 = "promoter_summit100bp_annotationConvertedIDs.txt" , GOenrich = "promoter_summit100bp_mm10GOenrich", kegg="promoter_summit100bp_KEGG.txt")
+
+
+#################################################### MOUSE ANNOTATION ##########################################################
+
+## Anotate peak files
+annotatePeaksmm10 <- function(peak, outFile, annoBar, distanceTSS, heatmapTSS, outFile1, GOenrich, kegg){
+  
+  # Annotate peak file based on mouse ENSEMBL annotation
+  peakAnno <- annotatePeak(peak, tssRegion = c(-3000, 3000), TxDb = TxDb.Mmusculus.UCSC.mm10.ensGene)
+  
+  # Write annotation to file
+  write.table(peakAnno, outFile, sep="\t", quote=F, row.names=F)
+
+  # Set promoter range
+  #promoter <- getPromoters(TxDb=TxDb.Mmusculus.UCSC.mm10.ensGene, upstream=3000, downstream=3000)
+  
+  # Find distance from TSS
+  #tagMatrix <- getTagMatrix(peak, windows=promoter)
+  
+  # Plot annotation
+  # pdf(file=annoBar, width=10, height=7)
+  # print(plotAnnoBar(peakAnno))
+  # dev.off()
+  
+  # Plot heatmap on peak distance to TSS
+  # pdf(file=heatmapTSS, width=10, height=7)
+  # print(tagHeatmap(tagMatrix, xlim=c(-3000, 3000), color = "#6699CC"))
+  # dev.off()
+
+  # Plot distance from TSS
+  # pdf(file=distanceTSS, width=10, height=7)
+  # print(plotDistToTSS(peakAnno))
+  # dev.off()
+
+  annot <- as.data.frame(peakAnno@anno)
+  
+  # GO enrichment analysis
+  GO <- enrichGO(gene = annot$geneId,
+                 keyType = "ENSEMBL",
+                 #universe = enh$geneId,
+                 OrgDb = org.Mm.eg.db,
+                 ont = "BP",
+                 pAdjustMethod = "BH",
+                 qvalueCutoff = 0.05,
+                 readable = TRUE)
+  write.table(GO, GOenrich, sep="\t", quote=F, row.names=F)
+  
+  ## convert mouse ensembl to entrezID
+  suppressWarnings(gene.df <- bitr(geneID=annot$geneId, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" ))
+  
+  ## kegg analysis
+  kk <- enrichKEGG(gene = gene.df$ENTREZID, 
+  #universe = enh$geneId, 
+  organism = "mmu", pvalueCutoff=0.05)
+  write.table(kk, kegg, sep="\t", quote=F, row.names=F)
+}
+
+
+### Consensus peaks
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers/")
+annotatePeaksmm10(peak = "E10.5_enhancer_peaks.narrowPeak", outFile = "E10.5_enhancer_annotation.txt", annoBar = "E10.5_enhancer_annoBar.pdf", distanceTSS = "E10.5_enhancer_distanceTSS.pdf", heatmapTSS = "E10.5_enhancer_heatmapTSS.pdf", outFile1 = "E10.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E10.5_enhancer_mm10GOenrich", kegg="E10.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_enhancer_peaks.narrowPeak", outFile = "E11.5_enhancer_annotation.txt", annoBar = "E11.5_enhancer_annoBar.pdf", distanceTSS = "E11.5_enhancer_distanceTSS.pdf", heatmapTSS = "E11.5_enhancer_heatmapTSS.pdf", outFile1 = "E11.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E11.5_enhancer_mm10GOenrich", kegg="E11.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_enhancer_peaks.narrowPeak", outFile = "E12.5_enhancer_annotation.txt", annoBar = "E12.5_enhancer_annoBar.pdf", distanceTSS = "E12.5_enhancer_distanceTSS.pdf", heatmapTSS = "E12.5_enhancer_heatmapTSS.pdf",  outFile1 = "E12.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E12.5_enhancer_mm10GOenrich", kegg="E12.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_enhancer_peaks.narrowPeak", outFile = "E13.5_enhancer_annotation.txt", annoBar = "E13.5_enhancer_annoBar.pdf", distanceTSS = "E13.5_enhancer_distanceTSS.pdf", heatmapTSS = "E13.5_enhancer_heatmapTSS.pdf",  outFile1 = "E13.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E13.5_enhancer_mm10GOenrich", kegg="E13.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_enhancer_peaks.narrowPeak", outFile = "E14.5_enhancer_annotation.txt", annoBar = "E14.5_enhancer_annoBar.pdf", distanceTSS = "E14.5_enhancer_distanceTSS.pdf", heatmapTSS = "E14.5_enhancer_heatmapTSS.pdf",  outFile1 = "E14.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E14.5_enhancer_mm10GOenrich", kegg="E14.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_enhancer_peaks.narrowPeak", outFile = "E15.5_enhancer_annotation.txt", annoBar = "E15.5_enhancer_annoBar.pdf", distanceTSS = "E15.5_enhancer_distanceTSS.pdf", heatmapTSS = "E15.5_enhancer_heatmapTSS.pdf",  outFile1 = "E15.5_enhancer_annotationConvertedIDs.txt", GOenrich = "E15.5_enhancer_mm10GOenrich", kegg="E15.5_enhancer_KEGG.txt")
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/promoters/")
+annotatePeaksmm10(peak = "E10.5_promoter_peaks.narrowPeak", outFile = "E10.5_promoter_annotation.txt", annoBar = "E10.5_promoter_annoBar.pdf", distanceTSS = "E10.5_promoter_distanceTSS.pdf", heatmapTSS = "E10.5_promoter_heatmapTSS.pdf",  GOenrich = "E10.5_promoter_mm10GOenrich", kegg="E10.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_promoter_peaks.narrowPeak", outFile = "E11.5_promoter_annotation.txt", annoBar = "E11.5_promoter_annoBar.pdf", distanceTSS = "E11.5_promoter_distanceTSS.pdf", heatmapTSS = "E11.5_promoter_heatmapTSS.pdf",  GOenrich = "E11.5_promoter_mm10GOenrich", kegg="E11.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_promoter_peaks.narrowPeak", outFile = "E12.5_promoter_annotation.txt", annoBar = "E12.5_promoter_annoBar.pdf", distanceTSS = "E12.5_promoter_distanceTSS.pdf", heatmapTSS = "E12.5_promoter_heatmapTSS.pdf",  GOenrich = "E12.5_promoter_mm10GOenrich", kegg="E12.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_promoter_peaks.narrowPeak", outFile = "E13.5_promoter_annotation.txt", annoBar = "E13.5_promoter_annoBar.pdf", distanceTSS = "E13.5_promoter_distanceTSS.pdf", heatmapTSS = "E13.5_promoter_heatmapTSS.pdf",  GOenrich = "E13.5_promoter_mm10GOenrich", kegg="E13.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_promoter_peaks.narrowPeak", outFile = "E14.5_promoter_annotation.txt", annoBar = "E14.5_promoter_annoBar.pdf", distanceTSS = "E14.5_promoter_distanceTSS.pdf", heatmapTSS = "E14.5_promoter_heatmapTSS.pdf",  GOenrich = "E14.5_promoter_mm10GOenrich", kegg="E14.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_promoter_peaks.narrowPeak", outFile = "E15.5_promoter_annotation.txt", annoBar = "E15.5_promoter_annoBar.pdf", distanceTSS = "E15.5_promoter_distanceTSS.pdf", heatmapTSS = "E15.5_promoter_heatmapTSS.pdf",  GOenrich = "E15.5_promoter_mm10GOenrich", kegg="E15.5_promoter_KEGG.txt")
+
+peakList = "consensus/promoters"
+peakFiles = list.files(peakList, pattern= "promoter_peaks.narrowPeak", full.names=T)
+peakData = lapply(peakFiles, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+peakData = list(peakData[[1]],peakData[[2]], peakData[[3]], peakData[[4]], peakData[[5]], peakData[[6]], peakData[[7]])
+names(peakData) = c("dunnart", "E10.5", "E11.5", "E12.5", "E13.5", "E14.5", "E15.5")
+
+clusterList = "consensus/promoters/clustered"
+clusterFiles = list.files(clusterList, pattern= "_annotation.txt", full.names=T)
+clusterData = lapply(clusterFiles, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+clusterIDs = lapply(clusterData, function(x) x$V4 %>% as.data.frame())
+clusterIDs = rbind(clusterIDs[[1]], clusterIDs[[2]], clusterIDs[[3]], clusterIDs[[4]], clusterIDs[[5]], clusterIDs[[6]], clusterIDs[[7]])
+test = as.vector(unlist(clusterIDs))
+promoters = lapply(peakData, function(x) x[x$V4 %in% test,])    
+lapply(names(promoters), function(x) write.table(promoters[[x]], file=paste(x,"clustered","narrowPeak", sep="."), sep="\t", quote=FALSE, col.names=FALSE, row.names=FALSE))
+
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+annotatePeaksmm10(peak = "E10.5.clustered.narrowPeak", outFile = "cluster1_E10.5_promoter_annotation.txt", annoBar = "cluster1_E10.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E10.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E10.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E10.5_promoter_mm10GOenrich", kegg="cluster1_E10.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5.clustered.narrowPeak", outFile = "cluster1_E11.5_promoter_annotation.txt", annoBar = "cluster1_E11.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E11.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E11.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E11.5_promoter_mm10GOenrich", kegg="cluster1_E11.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5.clustered.narrowPeak", outFile = "cluster1_E12.5_promoter_annotation.txt", annoBar = "cluster1_E12.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E12.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E12.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E12.5_promoter_mm10GOenrich", kegg="cluster1_E12.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5.clustered.narrowPeak", outFile = "cluster1_E13.5_promoter_annotation.txt", annoBar = "cluster1_E13.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E13.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E13.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E13.5_promoter_mm10GOenrich", kegg="cluster1_E13.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5.clustered.narrowPeak", outFile = "cluster1_E14.5_promoter_annotation.txt", annoBar = "cluster1_E14.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E14.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E14.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E14.5_promoter_mm10GOenrich", kegg="cluster1_E14.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5.clustered.narrowPeak", outFile = "cluster1_E15.5_promoter_annotation.txt", annoBar = "cluster1_E15.5_promoter_annoBar.pdf", distanceTSS = "cluster1_E15.5_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_E15.5_promoter_heatmapTSS.pdf", GOenrich = "cluster1_E15.5_promoter_mm10GOenrich", kegg="cluster1_E15.5_promoter_KEGG.txt")
+
+
+
+
+### Replicates
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/replicates/H3K27ac/")
+annotatePeaksmm10(peak = "ENCFF548BRR_vs_ENCFF825AVI_E10.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E10.5_ENCFF548BRR_H3K4me3_annoBar.pdf", distanceTSS = "E10.5_ENCFF548BRR_H3K27ac_distanceTSS.pdf", heatmapTSS = "E10.5_ENCFF548BRR_H3K27ac_heatmapTSS.pdf", outFile = "E10.5_ENCFF548BRR_H3K27ac_annotation.txt",   GOenrich = "E10.5_ENCFF548BRR_H3K27ac_mm10GOenrich.txt", kegg="E10.5_ENCFF548BRR_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF213EBC_vs_ENCFF157KEH_E10.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E10.5_ENCFF213EBC_H3K27ac_annoBar.pdf", distanceTSS = "E10.5_ENCFF213EBC_H3K27ac_distanceTSS.pdf", heatmapTSS = "E10.5_ENCFF213EBC_H3K27ac_heatmapTSS.pdf", outFile = "E10.5_ENCFF213EBC_H3K27ac_annotation.txt",  GOenrich = "E10.5_ENCFF213EBC_H3K27ac_mm10GOenrich.txt", kegg="E10.5_ENCFF213EBC_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF512SFE_vs_ENCFF184CUE_E11.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E11.5_ENCFF512SFE_H3K27ac_annoBar.pdf", distanceTSS = "E11.5_ENCFF512SFE_H3K27ac_distanceTSS.pdf", heatmapTSS = "E11.5_ENCFF512SFE_H3K27ac_heatmapTSS.pdf", outFile = "E11.5_ENCFF512SFE_H3K27ac_annotation.txt",  GOenrich = "E11.5_ENCFF512SFE_H3K27ac_mm10GOenrich.txt", kegg="E11.5_ENCFF512SFE_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF515PKL_vs_ENCFF376FGM_E11.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E11.5_ENCFF515PKL_H3K27ac_annoBar.pdf", distanceTSS = "E11.5_ENCFF515PKL_H3K27ac_distanceTSS.pdf", heatmapTSS = "E11.5_ENCFF515PKL_H3K27ac_heatmapTSS.pdf", outFile = "E11.5_ENCFF515PKL_H3K27ac_annotation.txt",  GOenrich = "E11.5_ENCFF515PKL_H3K27ac_mm10GOenrich.txt", kegg="E11.5_ENCFF515PKL_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF011NFM_vs_ENCFF058AUT_E12.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E12.5_ENCFF011NFM_H3K27ac_annoBar.pdf", distanceTSS = "E12.5_ENCFF011NFM_H3K27ac_distanceTSS.pdf", heatmapTSS = "E12.5_ENCFF011NFM_H3K27ac_heatmapTSS.pdf", outFile = "E12.5_ENCFF011NFM_H3K27ac_annotation.txt",  GOenrich = "E12.5_ENCFF011NFM_H3K27ac_mm10GOenrich.txt", kegg="E12.5_ENCFF011NFM_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF394TZN_vs_ENCFF203JQV_E12.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E12.5_ENCFF394TZN_H3K27ac_annoBar.pdf", distanceTSS = "E12.5_ENCFF394TZN_H3K27ac_distanceTSS.pdf", heatmapTSS = "E12.5_ENCFF394TZN_H3K27ac_heatmapTSS.pdf", outFile = "E12.5_ENCFF394TZN_H3K27ac_annotation.txt",  GOenrich = "E12.5_ENCFF394TZN_H3K27ac_mm10GOenrich.txt", kegg="E12.5_ENCFF394TZN_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF194ORC_vs_ENCFF117QRC_E13.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E13.5_ENCFF194ORC_H3K27ac_annoBar.pdf", distanceTSS = "E13.5_ENCFF194ORC_H3K27ac_distanceTSS.pdf", heatmapTSS = "E13.5_ENCFF194ORC_H3K27ac_heatmapTSS.pdf", outFile = "E13.5_ENCFF194ORC_H3K27ac_annotation.txt",  GOenrich = "E13.5_ENCFF194ORC_H3K27ac_mm10GOenrich.txt", kegg="E13.5_ENCFF194ORC_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF290ZNF_vs_ENCFF248PGK_E13.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E13.5_ENCFF290ZNF_H3K27ac_annoBar.pdf", distanceTSS = "E13.5_ENCFF290ZNF_H3K27ac_distanceTSS.pdf", heatmapTSS = "E13.5_ENCFF290ZNF_H3K27ac_heatmapTSS.pdf", outFile = "E13.5_ENCFF290ZNF_H3K27ac_annotation.txt",  GOenrich = "E13.5_ENCFF290ZNF_H3K27ac_mm10GOenrich.txt", kegg="E13.5_ENCFF290ZNF_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF902HAR_vs_ENCFF002HZV_E14.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E14.5_ENCFF902HAR_H3K27ac_annoBar.pdf", distanceTSS = "E14.5_ENCFF902HAR_H3K27ac_distanceTSS.pdf", heatmapTSS = "E14.5_ENCFF902HAR_H3K27ac_heatmapTSS.pdf", outFile = "E14.5_ENCFF902HAR_H3K27ac_annotation.txt",   GOenrich = "E14.5_ENCFF902HAR_H3K27ac_mm10GOenrich.txt", kegg="E14.5_ENCFF902HAR_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF327VAO_vs_ENCFF784ORI_E14.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E14.5_ENCFF327VAO_H3K27ac_annoBar.pdf", distanceTSS = "E14.5_ENCFF327VAO_H3K27ac_distanceTSS.pdf", heatmapTSS = "E14.5_ENCFF327VAO_H3K27ac_heatmapTSS.pdf", outFile = "E14.5_ENCFF327VAO_H3K27ac_annotation.txt",   GOenrich = "E14.5_ENCFF327VAO_H3K27ac_mm10GOenrich.txt", kegg="E14.5_ENCFF327VAO_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF584JFB_vs_ENCFF727QTS_E15.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E15.5_ENCFF584JFB_H3K27ac_annoBar.pdf", distanceTSS = "E15.5_ENCFF584JFB_H3K27ac_distanceTSS.pdf", heatmapTSS = "E15.5_ENCFF584JFB_H3K27ac_heatmapTSS.pdf", outFile = "E15.5_ENCFF584JFB_H3K27ac_annotation.txt",   GOenrich = "E15.5_ENCFF584JFB_H3K27ac_mm10GOenrich.txt", kegg="E15.5_ENCFF584JFB_H3K27ac_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF707WKL_vs_ENCFF182XFG_E15.5_H3K27ac_macs2_peaks.narrowPeak", annoBar = "E15.5_ENCFF707WKL_H3K27ac_annoBar.pdf", distanceTSS = "E15.5_ENCFF707WKL_H3K27ac_distanceTSS.pdf", heatmapTSS = "E15.5_ENCFF707WKL_H3K27ac_heatmapTSS.pdf", outFile = "E15.5_ENCFF707WKL_H3K27ac_annotation.txt",  GOenrich = "E15.5_ENCFF707WKL_H3K27ac_mm10GOenrich.txt", kegg="E15.5_ENCFF707WKL_H3K27ac_KEGG.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/replicates/H3K4me3/")
+annotatePeaksmm10(peak = "ENCFF124UYX_vs_ENCFF157KEH_E10.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E10.5_ENCFF124UYX_H3K4me3_annoBar.pdf", distanceTSS = "E10.5_ENCFF124UYX_H3K4me3_distanceTSS.pdf", heatmapTSS = "E10.5_ENCFF124UYX_H3K4me3_heatmapTSS.pdf", outFile = "E10.5_ENCFF124UYX_H3K4me3_annotation.txt",  GOenrich = "E10.5_ENCFF124UYX_H3K4me3_mm10GOenrich.txt", kegg="E10.5_ENCFF124UYX_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF045IPK_vs_ENCFF825AVI_E10.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E10.5_ENCFF045IPK_H3K4me3_annoBar.pdf", distanceTSS = "E10.5_ENCFF045IPK_H3K4me3_distanceTSS.pdf", heatmapTSS = "E10.5_ENCFF045IPK_H3K4me3_heatmapTSS.pdf", outFile = "E10.5_ENCFF045IPK_H3K4me3_annotation.txt",   GOenrich = "E10.5_ENCFF045IPK_H3K4me3_mm10GOenrich.txt", kegg="E10.5_ENCFF045IPK_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF717QDV_vs_ENCFF376FGM_E11.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E11.5_ENCFF717QDV_H3K4me3_annoBar.pdf", distanceTSS = "E11.5_ENCFF717QDV_H3K4me3_distanceTSS.pdf", heatmapTSS = "E11.5_ENCFF717QDV_H3K4me3_heatmapTSS.pdf", outFile = "E11.5_ENCFF717QDV_H3K4me3_annotation.txt",   GOenrich = "E11.5_ENCFF717QDV_H3K4me3_mm10GOenrich.txt", kegg="E11.5_ENCFF717QDV_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF760QYZ_vs_ENCFF184CUE_E11.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E11.5_ENCFF760QYZ_H3K4me3_annoBar.pdf", distanceTSS = "E11.5_ENCFF760QYZ_H3K4me3_distanceTSS.pdf", heatmapTSS = "E11.5_ENCFF760QYZ_H3K4me3_heatmapTSS.pdf", outFile = "E11.5_ENCFF760QYZ_H3K4me3_annotation.txt", GOenrich = "E11.5_ENCFF515PKL_H3K4me3_mm10GOenrich.txt", kegg="E11.5_ENCFF515PKL_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF182ZPF_vs_ENCFF203JQV_E12.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E12.5_ENCFF182ZPF_H3K4me3_annoBar.pdf", distanceTSS = "E12.5_ENCFF182ZPF_H3K4me3_distanceTSS.pdf", heatmapTSS = "E12.5_ENCFF182ZPF_H3K4me3_heatmapTSS.pdf", outFile = "E12.5_ENCFF182ZPF_H3K4me3_annotation.txt",  GOenrich = "E12.5_ENCFF182ZPF_H3K4me3_mm10GOenrich.txt", kegg="E12.5_ENCFF182ZPF_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF941QJZ_vs_ENCFF058AUT_E12.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E12.5_ENCFF941QJZ_H3K4me3_annoBar.pdf", distanceTSS = "E12.5_ENCFF941QJZ_H3K4me3_distanceTSS.pdf", heatmapTSS = "E12.5_ENCFF941QJZ_H3K4me3_heatmapTSS.pdf", outFile = "E12.5_ENCFF941QJZ_H3K4me3_annotation.txt",   GOenrich = "E12.5_ENCFF941QJZ_H3K4me3_mm10GOenrich.txt", kegg="E12.5_ENCFF941QJZ_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF124TAB_vs_ENCFF248PGK_E13.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E13.5_ENCFF124TAB_H3K4me3_annoBar.pdf", distanceTSS = "E13.5_ENCFF124TAB_H3K4me3_distanceTSS.pdf", heatmapTSS = "E13.5_ENCFF124TAB_H3K4me3_heatmapTSS.pdf", outFile = "E13.5_ENCFF124TAB_H3K4me3_annotation.txt",   GOenrich = "E13.5_ENCFF124TAB_H3K4me3_mm10GOenrich.txt", kegg="E13.5_ENCFF124TAB_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF485UDC_vs_ENCFF117QRC_E13.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E13.5_ENCFF485UDC_H3K4me3_annoBar.pdf", distanceTSS = "E13.5_ENCFF485UDC_H3K4me3_distanceTSS.pdf", heatmapTSS = "E13.5_ENCFF485UDC_H3K4me3_heatmapTSS.pdf", outFile = "E13.5_ENCFF485UDC_H3K4me3_annotation.txt", GOenrich = "E13.5_ENCFF485UDC_H3K4me3_mm10GOenrich.txt", kegg="E13.5_ENCFF485UDC_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF665QBJ_vs_ENCFF002HZV_E14.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E14.5_ENCFF665QBJ_H3K4me3_annoBar.pdf", distanceTSS = "E14.5_ENCFF665QBJ_H3K4me3_distanceTSS.pdf", heatmapTSS = "E14.5_ENCFF665QBJ_H3K4me3_heatmapTSS.pdf", outFile = "E14.5_ENCFF665QBJ_H3K4me3_annotation.txt",   GOenrich = "E14.5_ENCFF665QBJ_H3K4me3_mm10GOenrich.txt", kegg="E14.5_ENCFF665QBJ_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF724DMU_vs_ENCFF784ORI_E14.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E14.5_ENCFF724DMU_H3K4me3_annoBar.pdf", distanceTSS = "E14.5_ENCFF724DMU_H3K4me3_distanceTSS.pdf", heatmapTSS = "E14.5_ENCFF724DMU_H3K4me3_heatmapTSS.pdf", outFile = "E14.5_ENCFF724DMU_H3K4me3_annotation.txt",  GOenrich = "E14.5_ENCFF724DMU_H3K4me3_mm10GOenrich.txt", kegg="E14.5_ENCFF724DMU_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF258KCR_vs_ENCFF727QTS_E15.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E15.5_ENCFF258KCR_H3K4me3_annoBar.pdf", distanceTSS = "E15.5_ENCFF258KCR_H3K4me3_distanceTSS.pdf", heatmapTSS = "E15.5_ENCFF258KCR_H3K4me3_heatmapTSS.pdf", outFile = "E15.5_ENCFF258KCR_H3K4me3_annotation.txt",  GOenrich = "E15.5_ENCFF258KCR_H3K4me3_mm10GOenrich.txt", kegg="E15.5_ENCFF258KCR_H3K4me3_KEGG.txt")
+annotatePeaksmm10(peak = "ENCFF401BKM_vs_ENCFF182XFG_E15.5_H3K4me3_macs2_peaks.narrowPeak", annoBar = "E15.5_ENCFF401BKM_H3K4me3_annoBar.pdf", distanceTSS = "E15.5_ENCFF401BKM_H3K4me3_distanceTSS.pdf", heatmapTSS = "E15.5_ENCFF401BKM_H3K4me3_heatmapTSS.pdf", outFile = "E15.5_ENCFF401BKM_H3K4me3_annotation.txt",   GOenrich = "E15.5_ENCFF401BKM_H3K4me3_mm10GOenrich.txt", kegg="E15.5_ENCFF401BKM_H3K4me3_KEGG.txt")
+
+## Overlaping dunnart and mouse peaks
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+annotatePeaksmm10(peak = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10.bed", outFile = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annotation.txt", annoBar = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annoBar.pdf", distanceTSS = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_distanceTSS.pdf", heatmapTSS = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_heatmapTSS.pdf", GOenrich = "enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_mm10GOenrich", kegg="enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_KEGG.txt")
+annotatePeaksmm10(peak = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10.bed", outFile = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annotation.txt", annoBar = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annoBar.pdf", distanceTSS = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_distanceTSS.pdf", heatmapTSS = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_heatmapTSS.pdf", GOenrich = "promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_mm10GOenrich", kegg="promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_KEGG.txt")
+enh <- read.table("enhancer_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annotation.txt", sep="\t", header=TRUE, quote = "")
+pro <- read.table("promoter_summit100bp_mm10TOsmiCra1_smiCraTOmm10_annotation.txt", sep="\t", header=TRUE, quote = "")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/enhancers/")
+annotatePeaksmm10(peak = "E10.5_enhancerOverlap_100bpsummits.bed", outFile = "E10.5_enhancer_annotation.txt", annoBar = "E10.5_enhancer_annoBar.pdf", distanceTSS = "E10.5_enhancer_distanceTSS.pdf", heatmapTSS = "E10.5_enhancer_heatmapTSS.pdf",  outFile1 = "E10.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E10.5_enhancer_mm10GOenrich", kegg="E10.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_enhancerOverlap_100bpsummits.bed", outFile = "E11.5_enhancer_annotation.txt", annoBar = "E11.5_enhancer_annoBar.pdf", distanceTSS = "E11.5_enhancer_distanceTSS.pdf", heatmapTSS = "E11.5_enhancer_heatmapTSS.pdf",  outFile1 = "E11.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E11.5_enhancer_mm10GOenrich", kegg="E11.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_enhancerOverlap_100bpsummits.bed", outFile = "E12.5_enhancer_annotation.txt", annoBar = "E12.5_enhancer_annoBar.pdf", distanceTSS = "E12.5_enhancer_distanceTSS.pdf", heatmapTSS = "E12.5_enhancer_heatmapTSS.pdf",  outFile1 = "E12.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E12.5_enhancer_mm10GOenrich", kegg="E12.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_enhancerOverlap_100bpsummits.bed", outFile = "E13.5_enhancer_annotation.txt", annoBar = "E13.5_enhancer_annoBar.pdf", distanceTSS = "E13.5_enhancer_distanceTSS.pdf", heatmapTSS = "E13.5_enhancer_heatmapTSS.pdf",  outFile1 = "E13.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E13.5_enhancer_mm10GOenrich", kegg="E13.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_enhancerOverlap_100bpsummits.bed", outFile = "E14.5_enhancer_annotation.txt", annoBar = "E14.5_enhancer_annoBar.pdf", distanceTSS = "E14.5_enhancer_distanceTSS.pdf", heatmapTSS = "E14.5_enhancer_heatmapTSS.pdf",  outFile1 = "E14.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E14.5_enhancer_mm10GOenrich", kegg="E14.5_enhancer_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_enhancerOverlap_100bpsummits.bed", outFile = "E15.5_enhancer_annotation.txt", annoBar = "E15.5_enhancer_annoBar.pdf", distanceTSS = "E15.5_enhancer_distanceTSS.pdf", heatmapTSS = "E15.5_enhancer_heatmapTSS.pdf",  outFile1 = "E15.5_enhancer_annotationConvertedIDs.txt" , GOenrich = "E15.5_enhancer_mm10GOenrich", kegg="E15.5_enhancer_KEGG.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters/")
+annotatePeaksmm10(peak = "E10.5_promoterOverlap_100bpsummits.bed", outFile = "E10.5_promoter_annotation.txt", annoBar = "E10.5_promoter_annoBar.pdf", distanceTSS = "E10.5_promoter_distanceTSS.pdf", heatmapTSS = "E10.5_promoter_heatmapTSS.pdf",  outFile1 = "E10.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E10.5_promoter_mm10GOenrich", kegg="E10.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_promoterOverlap_100bpsummits.bed", outFile = "E11.5_promoter_annotation.txt", annoBar = "E11.5_promoter_annoBar.pdf", distanceTSS = "E11.5_promoter_distanceTSS.pdf", heatmapTSS = "E11.5_promoter_heatmapTSS.pdf",  outFile1 = "E11.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E11.5_promoter_mm10GOenrich", kegg="E11.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_promoterOverlap_100bpsummits.bed", outFile = "E12.5_promoter_annotation.txt", annoBar = "E12.5_promoter_annoBar.pdf", distanceTSS = "E12.5_promoter_distanceTSS.pdf", heatmapTSS = "E12.5_promoter_heatmapTSS.pdf",  outFile1 = "E12.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E12.5_promoter_mm10GOenrich", kegg="E12.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_promoterOverlap_100bpsummits.bed", outFile = "E13.5_promoter_annotation.txt", annoBar = "E13.5_promoter_annoBar.pdf", distanceTSS = "E13.5_promoter_distanceTSS.pdf", heatmapTSS = "E13.5_promoter_heatmapTSS.pdf",  outFile1 = "E13.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E13.5_promoter_mm10GOenrich", kegg="E13.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_promoterOverlap_100bpsummits.bed", outFile = "E14.5_promoter_annotation.txt", annoBar = "E14.5_promoter_annoBar.pdf", distanceTSS = "E14.5_promoter_distanceTSS.pdf", heatmapTSS = "E14.5_promoter_heatmapTSS.pdf",  outFile1 = "E14.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E14.5_promoter_mm10GOenrich", kegg="E14.5_promoter_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_promoterOverlap_100bpsummits.bed", outFile = "E15.5_promoter_annotation.txt", annoBar = "E15.5_promoter_annoBar.pdf", distanceTSS = "E15.5_promoter_distanceTSS.pdf", heatmapTSS = "E15.5_promoter_heatmapTSS.pdf",  outFile1 = "E15.5_promoter_annotationConvertedIDs.txt" , GOenrich = "E15.5_promoter_mm10GOenrich", kegg="E15.5_promoter_KEGG.txt")
+
+## No overlaps
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/enhancers/")
+annotatePeaksmm10(peak = "E10.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E10.5_enhancerNo_annotation.txt", annoBar = "E10.5_enhancerNo_annoBar.pdf", distanceTSS = "E10.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E10.5_enhancerNo_heatmapTSS.pdf",  GOenrich = "E10.5_enhancerNo_mm10GOenrich", kegg="E10.5_enhancerNo_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E11.5_enhancerNo_annotation.txt", annoBar = "E11.5_enhancerNo_annoBar.pdf", distanceTSS = "E11.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E11.5_enhancerNo_heatmapTSS.pdf",  GOenrich = "E11.5_enhancerNo_mm10GOenrich", kegg="E11.5_enhancerNo_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E12.5_enhancerNo_annotation.txt", annoBar = "E12.5_enhancerNo_annoBar.pdf", distanceTSS = "E12.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E12.5_enhancerNo_heatmapTSS.pdf",   GOenrich = "E12.5_enhancerNo_mm10GOenrich", kegg="E12.5_enhancerNo_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E13.5_enhancerNo_annotation.txt", annoBar = "E13.5_enhancerNo_annoBar.pdf", distanceTSS = "E13.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E13.5_enhancerNo_heatmapTSS.pdf",  GOenrich = "E13.5_enhancerNo_mm10GOenrich", kegg="E13.5_enhancerNo_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E14.5_enhancerNo_annotation.txt", annoBar = "E14.5_enhancerNo_annoBar.pdf", distanceTSS = "E14.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E14.5_enhancerNo_heatmapTSS.pdf",  GOenrich = "E14.5_enhancerNo_mm10GOenrich", kegg="E14.5_enhancerNo_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_enhancerNoOverlap_100bpsummits.bed", outFile = "E15.5_enhancerNo_annotation.txt", annoBar = "E15.5_enhancerNo_annoBar.pdf", distanceTSS = "E15.5_enhancerNo_distanceTSS.pdf", heatmapTSS = "E15.5_enhancerNo_heatmapTSS.pdf",  GOenrich = "E15.5_enhancerNo_mm10GOenrich", kegg="E15.5_enhancerNo_KEGG.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters/")
+annotatePeaksmm10(peak = "E10.5_promoterNoOverlap_100bpsummits.bed", outFile = "E10.5_promoterNo_annotation.txt", annoBar = "E10.5_promoterNo_annoBar.pdf", distanceTSS = "E10.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E10.5_promoterNo_heatmapTSS.pdf",  GOenrich = "E10.5_promoterNo_mm10GOenrich", kegg="E10.5_promoterNo_KEGG.txt")
+annotatePeaksmm10(peak = "E11.5_promoterNoOverlap_100bpsummits.bed", outFile = "E11.5_promoterNo_annotation.txt", annoBar = "E11.5_promoterNo_annoBar.pdf", distanceTSS = "E11.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E11.5_promoterNo_heatmapTSS.pdf",   GOenrich = "E11.5_promoterNo_mm10GOenrich", kegg="E11.5_promoterNo_KEGG.txt")
+annotatePeaksmm10(peak = "E12.5_promoterNoOverlap_100bpsummits.bed", outFile = "E12.5_promoterNo_annotation.txt", annoBar = "E12.5_promoterNo_annoBar.pdf", distanceTSS = "E12.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E12.5_promoterNo_heatmapTSS.pdf",  GOenrich = "E12.5_promoterNo_mm10GOenrich", kegg="E12.5_promoterNo_KEGG.txt")
+annotatePeaksmm10(peak = "E13.5_promoterNoOverlap_100bpsummits.bed", outFile = "E13.5_promoterNo_annotation.txt", annoBar = "E13.5_promoterNo_annoBar.pdf", distanceTSS = "E13.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E13.5_promoterNo_heatmapTSS.pdf", GOenrich = "E13.5_promoterNo_mm10GOenrich", kegg="E13.5_promoterNo_KEGG.txt")
+annotatePeaksmm10(peak = "E14.5_promoterNoOverlap_100bpsummits.bed", outFile = "E14.5_promoterNo_annotation.txt", annoBar = "E14.5_promoterNo_annoBar.pdf", distanceTSS = "E14.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E14.5_promoterNo_heatmapTSS.pdf",  GOenrich = "E14.5_promoterNo_mm10GOenrich", kegg="E14.5_promoterNo_KEGG.txt")
+annotatePeaksmm10(peak = "E15.5_promoterNoOverlap_100bpsummits.bed", outFile = "E15.5_promoterNo_annotation.txt", annoBar = "E15.5_promoterNo_annoBar.pdf", distanceTSS = "E15.5_promoterNo_distanceTSS.pdf", heatmapTSS = "E15.5_promoterNo_heatmapTSS.pdf",   GOenrich = "E15.5_promoterNo_mm10GOenrich", kegg="E15.5_promoterNo_KEGG.txt")
+
+## OVERLAP NEAREST GENE CALLS
+
+# Enhancers
+# E10.5, E11.5, E12.5, E13.5, E14.5, E15.5 and dunnart P0
+# Needs to be for the orthologous gene calls so use the convertedIDs file and filter for peaks that have an orthologous gene ID
+# use lapply to make a list of all the nearest gene calls into one 
+setwd("/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/2-CrossSpecies/4-Peaks/2-DunnartSubsample10Mreads/")
+
+samplefiles1 <- list.files("4-ConvertedIDs/1-Enhancers/", pattern= ".txt", full.names=T)
+samplefiles1 <- as.list(samplefiles1)
+names(samplefiles1) <- c("dunnart", "E15.5")
+data <- lapply(samplefiles1, function(x) read.table(x, header=TRUE, sep="\t", quote = ""))
+genes= lapply(data, function(x) unique(na.omit(as.data.frame(x)$mouseensembl)))
+vennplot(genes)
+
+
+# also look at conservation for regions that are both active versus those that aren't
+# what gene pathways are the ones that are active enriched for?
+# motif enrichment?
+
+## JACCARD INDEX 
+jaccard <- function(a, b) {
+    a <- scan(a, what="", sep="\n")
+    b <- scan(b, what="", sep="\n")
+    intersection = length(intersect(a, b))
+    union = length(a) + length(b) - intersection
+    return (intersection/union)
+}
+
+## Compare GO terms
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers/")
+jaccard(a="dunnart_enhancer_mm10GOenrich_GOids.txt", b="E10.5_enhancer_mm10GOenrich_GOids.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/promoters/")
+jaccard(a="dunnart_promoter_mm10GOenrich_GOids.txt", b="E15.5_promoter_mm10GOenrich_GOids.txt")
+
+## Compare gene lists
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers/")
+jaccard(a="dunnart_enhancer_geneId.txt", b="E15.5_enhancer_geneId.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/promoters/")
+jaccard(a="dunnart_promoter_geneId.txt", b="E10.5_promoter_geneId.txt")
+
+
diff --git a/comparePeaks.R b/comparePeaks.R
new file mode 100755
index 0000000000000000000000000000000000000000..9923fbd85519282443daf89b37fdc408bb68fb5a
--- /dev/null
+++ b/comparePeaks.R
@@ -0,0 +1,451 @@
+library(liftOver)
+library(ChIPpeakAnno)
+
+###################################################################################################
+###################################### COMPARING PEAKS ############################################
+###################################################################################################
+
+## All peaks
+system("less enhancers/dunnart_enhancer_peaks.narrowPeak | awk '{print $1,$2,$3}' > dunnart.enhancer_peaks_coord.bed",intern=TRUE)
+system("less promoters/clustered/dunnart.clustered.narrowPeak | awk '{print $1,$2,$3}' > promoters/clustered/dunnart.promoter_peaks_coord.bed",intern=TRUE)
+
+system("liftOver enhancers/dunnart.enhancer_peaks_coord.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart.enhancer_smiCraTOmm10.bed enhancer_smiCraTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/dunnart.promoter_peaks_coord.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart.promoter_smiCraTOmm10.bed promoter_smiCraTOmm10_unmapped.bed",intern=TRUE)
+# enhancer = 437 peaks
+# promoter = 48 peaks
+
+
+## LiftOver peaks allowing for multiple matches
+## Minimum target size is 50bp
+## Minimum query size is 50bp
+
+system("liftOver -bedPlus=3 -multiple -minChainT=100 -minChainQ=100 enhancers/dunnart_enhancer_peaks.narrowPeak ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/dunnart.enhancer_smiCraTOmm10.bed enhancers/enhancer_smiCraTOmm10_unmapped.bed",intern=TRUE)
+## 29347
+system("liftOver -bedPlus=3 -multiple -minChainT=100 -minChainQ=100 promoters/clustered/dunnart.clustered.narrowPeak ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/dunnart.promoter_smiCraTOmm10.bed promoters/clustered/promoter_smiCraTOmm10_unmapped.bed",intern=TRUE)
+## 20191
+
+# Import output files
+enh = fread("enhancers/dunnart.enhancer_smiCraTOmm10.bed")
+prom = fread("promoters/clustered/dunnart.promoter_smiCraTOmm10.bed")
+data = list(enh, prom)
+names(data) = c("enhancers", "promoters")
+data = Map(mutate, data, cre = names(data))
+data = lapply(data, function(x) x[order(V4),])
+data = lapply(data, function(x) x=x[,width:=V3-V2])
+data = rbindlist(data,)
+
+
+###################################################################################################
+################################### EXTRACT PEAK SUMMITS ########################################
+###################################################################################################
+
+# make list of peaks enhancer and promoter groups
+system("less enhancers/dunnart_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}' | sort > enhancers/enhancer_peak_IDs.txt", intern=TRUE)
+system("less promoters/clustered/dunnart.clustered.narrowPeak | awk -F '\t' '{print $4}' | sort > promoters/clustered/promoter_peak_IDs.txt", intern=TRUE)
+
+system("cat /data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/H3K4me3_pooled_macs2_summits.bed /data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/H3K27ac_pooled_macs2_summits.bed > H3K4me3_H3K27ac_combinedSummits.bed", intern=TRUE)
+
+system("less E10.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E10.5_enhancer_peaks_IDs.txt",intern=TRUE)
+system("less E11.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E11.5_enhancer_peaks_IDs.txt",intern=TRUE)
+system("less E12.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E12.5_enhancer_peaks_IDs.txt",intern=TRUE)
+system("less E13.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E13.5_enhancer_peaks_IDs.txt",intern=TRUE)
+system("less E14.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E14.5_enhancer_peaks_IDs.txt",intern=TRUE)
+system("less E15.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E15.5_enhancer_peaks_IDs.txt",intern=TRUE)
+
+system("less  E10.5.clustered.narrowPeak | awk -F '\t' '{print $4}' > promoters/clustered/E10.5_promoter_peaks_IDs.txt",intern=TRUE)
+system("less  E11.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E11.5_promoter_peaks_IDs.txt",intern=TRUE)
+system("less  E12.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E12.5_promoter_peaks_IDs.txt",intern=TRUE)
+system("less  E13.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E13.5_promoter_peaks_IDs.txt",intern=TRUE)
+system("less  E14.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E14.5_promoter_peaks_IDs.txt",intern=TRUE)
+system("less  E15.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E15.5_promoter_peaks_IDs.txt",intern=TRUE)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/mouse/results_10M/macs2")
+system("cat E10.5_H3K27ac.pooled.macs2_summits.bed E10.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E10.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+system("cat E11.5_H3K27ac.pooled.macs2_summits.bed E11.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E11.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+system("cat E12.5_H3K27ac.pooled.macs2_summits.bed E12.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E12.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+system("cat E13.5_H3K27ac.pooled.macs2_summits.bed E13.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E13.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+system("cat E14.5_H3K27ac.pooled.macs2_summits.bed E14.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E14.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+system("cat E15.5_H3K27ac.pooled.macs2_summits.bed E15.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E15.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed", intern=TRUE)
+
+# in R extract rows from summits file based on the list of peaks in overlap file for both enhancers and promoters
+## Dunnart
+
+summits <- read.table("H3K4me3_H3K27ac_combinedSummits.bed")
+reps1 <- scan("enhancers/enhancer_peak_IDs.txt", what="", sep="\n")
+reps2 <- scan("promoters/clustered/promoter_peak_IDs.txt", what="", sep="\n")
+enh_summits <- subset(summits, V4 %in% reps1)
+pro_summits <- subset(summits, V4 %in% reps2)
+write.table(enh_summits, "dunnart.enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(pro_summits, "dunnart.promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+## Mouse
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus")
+E10 <- read.table("E10.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E11<- read.table("E11.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E12<- read.table("E12.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E13<- read.table("E13.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E14<- read.table("E14.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E15<- read.table("E15.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+
+p10 <- scan("promoters/clustered/E10.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p11 <- scan("promoters/clustered/E11.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p12 <- scan("promoters/clustered/E12.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p13 <- scan("promoters/clustered/E13.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p14 <- scan("promoters/clustered/E14.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p15 <- scan("promoters/clustered/E15.5_promoter_peaks_IDs.txt", what="", sep="\n")
+
+e10 <- scan("enhancers/E10.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e11 <- scan("enhancers/E11.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e12 <- scan("enhancers/E12.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e13 <- scan("enhancers/E13.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e14 <- scan("enhancers/E14.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e15 <- scan("enhancers/E15.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+
+write.table(subset(E10, V4 %in% p10), "promoters/clustered/E10_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E11, V4 %in% p11), "promoters/clustered/E11_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E12, V4 %in% p12), "promoters/clustered/E12_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E13, V4 %in% p13), "promoters/clustered/E13_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E14, V4 %in% p14), "promoters/clustered/E14_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E15, V4 %in% p15), "promoters/clustered/E15_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+write.table(subset(E10, V4 %in% e10), "enhancers/E10_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E11, V4 %in% e11), "enhancers/E11_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E12, V4 %in% e12), "enhancers/E12_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E13, V4 %in% e13), "enhancers/E13_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E14, V4 %in% e14), "enhancers/E14_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E15, V4 %in% e15), "enhancers/E15_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+
+# in bash use bedtools to extend the summits 25bp in both directions to have 50bp 
+system("bedtools slop -i enhancers/dunnart.enhancer_overlap_summits.bed -b 25 -g smiCra1.chrom.sizes > enhancers/enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/dunnart.promoter_overlap_summits.bed -b 25 -g smiCra1.chrom.sizes > promoters/clustered/promoter_overlap_50bpsummits.bed", intern=TRUE)
+
+system("bedtools slop -i enhancers/E10_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E10_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i enhancers/E11_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E11_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i enhancers/E12_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E12_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i enhancers/E13_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E13_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i enhancers/E14_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E14_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i enhancers/E15_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E15_enhancer_overlap_50bpsummits.bed", intern=TRUE)
+
+system("bedtools slop -i promoters/clustered/E10_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E10_promoter_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/E11_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E11_promoter_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/E12_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E12_promoter_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/E13_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E13_promoter_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/E14_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E14_promoter_overlap_50bpsummits.bed", intern=TRUE)
+system("bedtools slop -i promoters/clustered/E15_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E15_promoter_overlap_50bpsummits.bed", intern=TRUE)
+
+
+###################################################################################################
+################################### LIFTOVER PEAK SUMMITS ########################################
+###################################################################################################
+
+#### COMBINED PEAKS - ACTIVE IN BOTH DUNNART AND MOUSE ####
+
+# LiftOver dunnart peaks to mouse genome
+system("liftOver enhancers/enhancer_overlap_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/enhancer_summit50bp_smiCraTOmm10.bed enhancers/enhancer_summit50bp_smiCraTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/promoter_overlap_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed promoters/clustered/promoter_summit50bp_smiCraTOmm10_unmapped.bed",intern=TRUE)
+
+## Intersect dunnart CREs with mouse CREs.
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak -wo > promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E11.5.clustered.narrowPeak -wo > promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E12.5.clustered.narrowPeak -wo > promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E13.5.clustered.narrowPeak -wo > promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E14.5.clustered.narrowPeak -wo > promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E15.5.clustered.narrowPeak -wo > promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits.bed",intern=TRUE)
+
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E10.5_enhancer_peaks.narrowPeak -wo > E10.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E11.5_enhancer_peaks.narrowPeak -wo > E11.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E12.5_enhancer_peaks.narrowPeak -wo > E12.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E13.5_enhancer_peaks.narrowPeak -wo > E13.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E14.5_enhancer_peaks.narrowPeak -wo > E14.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E15.5_enhancer_peaks.narrowPeak -wo > E15.5_enhancerOverlapmm10_50bpsummits.bed",intern=TRUE)
+
+## Lift dunnart peaks back over to the dunnart genome
+system("liftOver -bedPlus=4 promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+
+system("liftOver -bedPlus=4 enhancers/E10.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E11.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E12.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E13.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E14.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E15.5_enhancerOverlapmm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+
+# E11.5_H3K27ac.pooled.macs2_peak_13476 is duplicated
+# scaffold00030_pilon_pilon       15206485        15206536        H3K27ac_pooled_macs2_peak_99049 13.07934        chr16   44615130        44617625        E11.5_H3K27ac.pooled.macs2_peak_13476       269     .       10.16625        26.91089        23.58920        1194    51
+# scaffold00030_pilon_pilon       15207575        15207626        H3K27ac_pooled_macs2_peak_99050 28.62918        chr16   44615130        44617625        E11.5_H3K27ac.pooled.macs2_peak_13476       269     .       10.16625        26.91089        23.58920        1194    51
+
+# E11.5_H3K27ac.pooled.macs2_peak_10317 is duplicated
+# scaffold00002_pilon_pilon       130541237       130541288       H3K27ac_pooled_macs2_peak_13645 10.08028        chr14   24825909        24828470        E11.5_H3K27ac.pooled.macs2_peak_10317       372     .       12.48566        37.24145        33.69604        1973    52
+# scaffold00164_pilon_pilon       1438896 1438947 H3K27ac_pooled_macs2_peak_150856        6.25556 chr14   24825909        24828470        E11.5_H3K27ac.pooled.macs2_peak_10317       372     .       12.48566        37.24145        33.69604        1973    51
+
+# E12.5_H3K27ac.pooled.macs2_peak_25988 is duplicated
+# scaffold00030_pilon_pilon       15206485        15206536        H3K27ac_pooled_macs2_peak_99049 13.07934        chr16   44615052        44618395        E12.5_H3K27ac.pooled.macs2_peak_25988       428     .       14.73608        42.88547        39.11777        1216    51
+# scaffold00030_pilon_pilon       15207575        15207626        H3K27ac_pooled_macs2_peak_99050 28.62918        chr16   44615052        44618395        E12.5_H3K27ac.pooled.macs2_peak_25988       428     .       14.73608        42.88547        39.11777        1216    51
+
+# E13.5_H3K27ac.pooled.macs2_peak_35104 is duplicated
+# scaffold00030_pilon_pilon       15206485        15206536        H3K27ac_pooled_macs2_peak_99049 13.07934        chr16   44615114        44617699        E13.5_H3K27ac.pooled.macs2_peak_35104       357     .       9.00248 35.75615        32.52668        1721    51
+# scaffold00030_pilon_pilon       15207575        15207626        H3K27ac_pooled_macs2_peak_99050 28.62918        chr16   44615114        44617699        E13.5_H3K27ac.pooled.macs2_peak_35104       357     .       9.00248 35.75615        32.52668        1721    51
+
+# E13.5_H3K27ac.pooled.macs2_peak_26554 is duplicated
+# scaffold00002_pilon_pilon       130541237       130541288       H3K27ac_pooled_macs2_peak_13645 10.08028        chr14   24825799        24828390        E13.5_H3K27ac.pooled.macs2_peak_26554       324     .       11.76375        32.42406        29.25455        758     52
+# scaffold00164_pilon_pilon       1438896 1438947 H3K27ac_pooled_macs2_peak_150856        6.25556 chr14   24825799        24828390        E13.5_H3K27ac.pooled.macs2_peak_26554       324     .       11.76375        32.42406        29.25455        758     51
+
+# E14.5_H3K27ac.pooled.macs2_peak_24514 is duplicated
+# scaffold00030_pilon_pilon       15206485        15206536        H3K27ac_pooled_macs2_peak_99049 13.07934        chr16   44614943        44618063        E14.5_H3K27ac.pooled.macs2_peak_24514       544     .       11.99538        54.44302        51.09379        1990    51
+# scaffold00030_pilon_pilon       15207575        15207626        H3K27ac_pooled_macs2_peak_99050 28.62918        chr16   44614943        44618063        E14.5_H3K27ac.pooled.macs2_peak_24514       544     .       11.99538        54.44302        51.09379        1990    51
+
+# E14.5_H3K27ac.pooled.macs2_peak_18551 is duplicated
+# scaffold00002_pilon_pilon       130541237       130541288       H3K27ac_pooled_macs2_peak_13645 10.08028        chr14   24825764        24828307        E14.5_H3K27ac.pooled.macs2_peak_18551       263     .       10.19783        26.37918        23.49332        1742    52
+# scaffold00164_pilon_pilon       1438896 1438947 H3K27ac_pooled_macs2_peak_150856        6.25556 chr14   24825764        24828307        E14.5_H3K27ac.pooled.macs2_peak_18551       263     .       10.19783        26.37918        23.49332        1742    51
+
+# E15.5_H3K27ac.pooled.macs2_peak_20587 is duplicated
+# scaffold00030_pilon_pilon       15206485        15206536        H3K27ac_pooled_macs2_peak_99049 13.07934        chr16   44615161        44617589        E15.5_H3K27ac.pooled.macs2_peak_20587       254     .       10.70462        25.49718        22.21071        1155    51
+# scaffold00030_pilon_pilon       15207575        15207626        H3K27ac_pooled_macs2_peak_99050 28.62918        chr16   44615161        44617589        E15.5_H3K27ac.pooled.macs2_peak_20587       254     .       10.70462        25.49718        22.21071        1155    51
+
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+
+# E14.5_H3K4me3.pooled.macs2_peak_8068 is duplicated
+# scaffold00098_pilon_pilon       5322430 5322481 H3K4me3_pooled_macs2_peak_110310        7.99621 chr15   8443049 8445832 E14.5_H3K4me3.pooled.macs2_peak_8068    3775    .  73.91695 377.57697       372.49487       1968    51
+# scaffold00200_pilon_pilon       318471  318522  H3K4me3_pooled_macs2_peak_125887        197.97865       chr15   8443049 8445832 E14.5_H3K4me3.pooled.macs2_peak_8068    3775.       73.91695        377.57697       372.49487       1968    53
+
+#### DUNNART SPECIFIC PEAKS - ACTIVE ONLY IN THE DUNNART ####
+## Intersect dunnart CREs with mouse CREs keeping those that DO NOT overlap.
+
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak -v > promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E11.5.clustered.narrowPeak -v > promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E12.5.clustered.narrowPeak -v > promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E13.5.clustered.narrowPeak -v > promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E14.5.clustered.narrowPeak -v > promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E15.5.clustered.narrowPeak -v > promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E10.5_enhancer_peaks.narrowPeak -v > enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E11.5_enhancer_peaks.narrowPeak -v > enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E12.5_enhancer_peaks.narrowPeak -v > enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E13.5_enhancer_peaks.narrowPeak -v > enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E14.5_enhancer_peaks.narrowPeak -v > enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E15.5_enhancer_peaks.narrowPeak -v > enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits.bed",intern=TRUE)
+
+## Lift dunnart-specific peaks back over to the dunnart genome
+system("liftOver -bedPlus=4 promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+
+system("liftOver -bedPlus=4 enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed",intern=TRUE)
+
+
+#### MOUSE SPECIFIC PEAKS - ACTIVE ONLY IN THE MOUSE ####
+
+# LiftOver mouse peaks to dunnart genome
+system("liftOver enhancers/E10_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E10_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver enhancers/E11_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E11_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver enhancers/E12_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E12_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver enhancers/E13_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E13_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver enhancers/E14_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E14_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver enhancers/E15_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E15_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+
+system("liftOver promoters/clustered/E10_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/E11_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/E12_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/E13_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/E14_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+system("liftOver promoters/clustered/E15_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra_unmapped.bed",intern=TRUE)
+
+## Intersect dunnart CREs with mouse CREs keeping those that DO NOT overlap.
+system("bedtools intersect -a promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+
+system("bedtools intersect -a enhancers/E10_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/E11_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/E12_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/E13_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/E14_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+system("bedtools intersect -a enhancers/E15_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits.bed",intern=TRUE)
+
+## Lift mouse-specific peaks back over to the mouse genome
+system("liftOver -bedPlus=4 promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+
+system("liftOver -bedPlus=4 enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed",intern=TRUE)
+
+## Lift dunnart-specific peaks and combined peaks back into mm10 coordinates for GREAT analysis
+system("liftOver -bedPlus=4 promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+
+system("liftOver -bedPlus=4 enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+
+## Combined peaks
+
+system("liftOver -bedPlus=4 promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+
+system("liftOver -bedPlus=4 enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+system("liftOver -bedPlus=4 enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed",intern=TRUE)
+
+
+###################################################################################################
+################################### INTERSECT IDS FROM OVERLAPS ###################################
+###################################################################################################
+
+
+
+
+
+###################################################################################################
+############################### GENE ONTOLOGY & PATHWAY ANALYSIS ##################################
+###################################################################################################
+##  Using mouse coordinates for the peaks, use GREAT to look for the nearest gene TSS
+##  Then use clusterProfiler to look at Gene Ontology enrichment in the mouse, dunnart and combined peak sets
+
+library(rGREAT)
+library(clusterProfiler)
+library(data.table)
+
+
+great <- function(bedList){
+    bedFiles = list.files(bedList, pattern= ".bed", full.names=T) # create list of files in directory
+    bedFiles = as.list(bedFiles)
+    data = lapply(bedFiles, function(x) fread(x, header=TRUE, sep="\t", quote = "") %>% as.data.table()) # read in all files
+    data = lapply(data, function(x) submitGreatJob(x, species = "mm10")) # submit GREAT jobs
+    names(data) = c("E10:mouse", "E10:combined", "E10:dunnart", "E11:mouse", "E11:combined", "E11:dunnart",
+    "E12:mouse", "E12:combined", "E12:dunnart", "E13:mouse", "E13:combined", "E13:dunnart", 
+    "E14:mouse", "E14:combined", "E14:dunnart", "E15:mouse", "E15:combined", "E15:dunnart")
+
+    data = Map(mutate, data, group = names(data)) ## add column with group names
+
+    databind = rbindlist(data,) #bind dataframes together
+    split_by_stage = databind[
+     ,c("stage", "species") := tstrsplit(group, ":", fixed=TRUE)
+     ][
+       ,'group':=NULL
+       ]
+
+
+}
+
+
+
+
+## For each mouse stage have a list of peak IDs
+
+# ## Scan IDs into an object
+# setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers")
+# E10 <- scan("E10.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E11 <- scan("E11.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E12 <- scan("E12.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E13 <- scan("E13.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E14 <- scan("E14.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E15 <- scan("E15.5_enhancerOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+
+# ## Look for dunnart peak IDs that overlap with mouse embryonic stages in the mouse peaks that overlap with the dunnart peaks at P0
+# # E10.5_enhancerOverlapsmiCra_50bpsummits.bed file:
+# # chr8    121436317       121436374       H3K27ac_pooled_macs2_peak_644   9.64793 chr8    121436230       121436873       E10.5_H3K27ac.pooled.macs2_peak_77013   75      .       4.66414 7.52876 5.17964 305     57
+
+# write.table(subset(read.table("E10.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E10) %in% V4), file = "E10.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E11.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E11) %in% V4), file = "E11.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E12.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E12) %in% V4), file = "E12.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E13.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E13) %in% V4), file = "E13.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E14.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E14) %in% V4), file = "E14.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E15.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E15) %in% V4), file = "E15.5_enhancerOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+# ## Mouse CREs with dunnart coordinates that aren't active in dunnart at P0
+# ## Do I use only those that are reproducible (eg. found in both lists)or even those that I overlapped from mouse to dunnart but not in the finally overlap??
+
+# write.table(subset(read.table("E10.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E10.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E11.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E11.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E12.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4), file = "E12.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E13.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E13.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E14.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E14.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E15.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E15.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+# ## Not in list dunnart CREs
+# write.table(subset(read.table("E10.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(E10 %in% V4)), file = "E10.5_enhancerNoOverlasmiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E11.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(V4 %in% E11)), file = "E11.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E12.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(V4 %in% E12), file = "E12.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E13.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(V4 %in% E13)), file = "E13.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E14.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(V4 %in% E14)), file = "E14.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E15.5_enhancerOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), !(V4 %in% E15)), file = "E15.5_enhancerNoOverlapmm10_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+# ## Promoters
+# setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/overlaps/promoters/clustered/")
+# E10 <- scan("E10.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E11 <- scan("E11.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E12 <- scan("E12.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E13 <- scan("E13.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E14 <- scan("E14.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+# E15 <- scan("E15.5_promoterOverlapmm10_50bpsummitsIDs.txt", what="", sep="\n")
+
+# ## Look for dunnart peak IDs that overlap with mouse embryonic stages in the mouse peaks that overlap with the dunnart peaks at P0
+# write.table(subset(read.table("E10.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E10) %in% V4), file = "E10.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E11.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E11) %in% V4), file = "E11.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E12.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E12) %in% V4), file = "E12.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E13.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E13) %in% V4), file = "E13.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E14.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E14) %in% V4), file = "E14.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+# write.table(subset(read.table("E15.5_promoterOverlapsmiCra_50bpsummits.bed", sep="\t", quote="", header=FALSE), unique(E15) %in% V4), file = "E15.5_promoterOverlapmm10_smiCra1_50bpsummits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+# ## Check duplicates
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E10.5_enhancerOverlapmm10_50bpsummits.bed
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E11.5_enhancerOverlapmm10_50bpsummits.bed
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E12.5_enhancerOverlapmm10_50bpsummits.bed
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E13.5_enhancerOverlapmm10_50bpsummits.bed
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E15.5_enhancerOverlapmm10_50bpsummits.bed
+# awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E14.5_enhancerOverlapmm10_50bpsummits.bed
+
+
+
+
+# awk 'x[$9]++ == 1 { print $9 " is duplicated"}' E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
\ No newline at end of file
diff --git a/comparePeaks.sh b/comparePeaks.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8e958a8fa93ba9ea73e3467f8beba7c88d741722
--- /dev/null
+++ b/comparePeaks.sh
@@ -0,0 +1,400 @@
+
+
+###################################################################################################
+###################################### COMPARING PEAKS ############################################
+###################################################################################################
+
+## All peaks
+less enhancers/dunnart_enhancer_peaks.narrowPeak | awk '{print $1,$2,$3}' > dunnart.enhancer_peaks_coord.bed
+less promoters/clustered/dunnart.clustered.narrowPeak | awk '{print $1,$2,$3}' > promoters/clustered/dunnart.promoter_peaks_coord.bed
+
+liftOver enhancers/dunnart.enhancer_peaks_coord.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart.enhancer_smiCraTOmm10.bed enhancer_smiCraTOmm10_unmapped.bed
+liftOver promoters/clustered/dunnart.promoter_peaks_coord.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart.promoter_smiCraTOmm10.bed promoter_smiCraTOmm10_unmapped.bed
+# enhancer = 437 peaks
+# promoter = 48 peaks
+
+# Import output files in R
+enh = fread("enhancers/dunnart.enhancer_smiCraTOmm10.bed")
+prom = fread("promoters/clustered/dunnart.promoter_smiCraTOmm10.bed")
+data = list(enh, prom)
+names(data) = c("enhancers", "promoters")
+data = Map(mutate, data, cre = names(data))
+data = lapply(data, function(x) x[order(V4),])
+data = lapply(data, function(x) x=x[,width:=V3-V2])
+data = rbindlist(data,)
+
+
+###################################################################################################
+################################### EXTRACT PEAK SUMMITS ########################################
+###################################################################################################
+
+# make list of peaks enhancer and promoter groups
+less enhancers/dunnart_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}' | sort > enhancers/enhancer_peak_IDs.txt
+less promoters/clustered/dunnart.clustered.narrowPeak | awk -F '\t' '{print $4}' | sort > promoters/clustered/promoter_peak_IDs.txt
+
+cat /data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/H3K4me3_pooled_macs2_summits.bed /data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/H3K27ac_pooled_macs2_summits.bed > H3K4me3_H3K27ac_combinedSummits.bed
+
+less E10.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E10.5_enhancer_peaks_IDs.txt
+less E11.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E11.5_enhancer_peaks_IDs.txt
+less E12.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E12.5_enhancer_peaks_IDs.txt
+less E13.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E13.5_enhancer_peaks_IDs.txt
+less E14.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E14.5_enhancer_peaks_IDs.txt
+less E15.5_enhancer_peaks.narrowPeak | awk -F '\t' '{print $4}'  > E15.5_enhancer_peaks_IDs.txt
+
+less  E10.5.clustered.narrowPeak | awk -F '\t' '{print $4}' > promoters/clustered/E10.5_promoter_peaks_IDs.txt
+less  E11.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E11.5_promoter_peaks_IDs.txt
+less  E12.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E12.5_promoter_peaks_IDs.txt
+less  E13.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E13.5_promoter_peaks_IDs.txt
+less  E14.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E14.5_promoter_peaks_IDs.txt
+less  E15.5.clustered.narrowPeak | awk -F '\t' '{print $4}'  > promoters/clustered/E15.5_promoter_peaks_IDs.txt
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/mouse/results_10M/macs2")
+cat E10.5_H3K27ac.pooled.macs2_summits.bed E10.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E10.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+cat E11.5_H3K27ac.pooled.macs2_summits.bed E11.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E11.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+cat E12.5_H3K27ac.pooled.macs2_summits.bed E12.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E12.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+cat E13.5_H3K27ac.pooled.macs2_summits.bed E13.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E13.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+cat E14.5_H3K27ac.pooled.macs2_summits.bed E14.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E14.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+cat E15.5_H3K27ac.pooled.macs2_summits.bed E15.5_H3K4me3.pooled.macs2_summits.bed > ../../../cross_species/peakAnnotation/consensus/E15.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed
+
+# in R extract rows from summits file based on the list of peaks in overlap file for both enhancers and promoters
+## Dunnart
+summits <- read.table("H3K4me3_H3K27ac_combinedSummits.bed")
+reps1 <- scan("enhancers/enhancer_peak_IDs.txt", what="", sep="\n")
+reps2 <- scan("promoters/clustered/promoter_peak_IDs.txt", what="", sep="\n")
+enh_summits <- subset(summits, V4 %in% reps1)
+pro_summits <- subset(summits, V4 %in% reps2)
+write.table(enh_summits, "dunnart.enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(pro_summits, "dunnart.promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+## Mouse
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus")
+E10 <- read.table("E10.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E11<- read.table("E11.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E12<- read.table("E12.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E13<- read.table("E13.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E14<- read.table("E14.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+E15<- read.table("E15.5_H3K27ac_H3K4me3.pooled.macs2_summits.bed")
+
+p10 <- scan("promoters/clustered/E10.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p11 <- scan("promoters/clustered/E11.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p12 <- scan("promoters/clustered/E12.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p13 <- scan("promoters/clustered/E13.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p14 <- scan("promoters/clustered/E14.5_promoter_peaks_IDs.txt", what="", sep="\n")
+p15 <- scan("promoters/clustered/E15.5_promoter_peaks_IDs.txt", what="", sep="\n")
+
+e10 <- scan("enhancers/E10.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e11 <- scan("enhancers/E11.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e12 <- scan("enhancers/E12.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e13 <- scan("enhancers/E13.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e14 <- scan("enhancers/E14.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+e15 <- scan("enhancers/E15.5_enhancer_peaks_IDs.txt", what="", sep="\n")
+
+write.table(subset(E10, V4 %in% p10), "promoters/clustered/E10_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E11, V4 %in% p11), "promoters/clustered/E11_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E12, V4 %in% p12), "promoters/clustered/E12_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E13, V4 %in% p13), "promoters/clustered/E13_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E14, V4 %in% p14), "promoters/clustered/E14_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E15, V4 %in% p15), "promoters/clustered/E15_promoter_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+write.table(subset(E10, V4 %in% e10), "enhancers/E10_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E11, V4 %in% e11), "enhancers/E11_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E12, V4 %in% e12), "enhancers/E12_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E13, V4 %in% e13), "enhancers/E13_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E14, V4 %in% e14), "enhancers/E14_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+write.table(subset(E15, V4 %in% e15), "enhancers/E15_enhancer_overlap_summits.bed", quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+
+
+# in bash use bedtools to extend the summits 25bp in both directions to have 50bp 
+bedtools slop -i enhancers/dunnart.enhancer_overlap_summits.bed -b 25 -g smiCra1.chrom.sizes > enhancers/enhancer_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/dunnart.promoter_overlap_summits.bed -b 25 -g smiCra1.chrom.sizes > promoters/clustered/promoter_overlap_50bpsummits.bed
+
+bedtools slop -i enhancers/E10_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E10_enhancer_overlap_50bpsummits.bed
+bedtools slop -i enhancers/E11_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E11_enhancer_overlap_50bpsummits.bed
+bedtools slop -i enhancers/E12_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E12_enhancer_overlap_50bpsummits.bed
+bedtools slop -i enhancers/E13_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E13_enhancer_overlap_50bpsummits.bed
+bedtools slop -i enhancers/E14_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E14_enhancer_overlap_50bpsummits.bed
+bedtools slop -i enhancers/E15_enhancer_overlap_summits.bed -b 25 -g mm10.chrom.sizes > enhancers/E15_enhancer_overlap_50bpsummits.bed
+
+bedtools slop -i promoters/clustered/E10_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E10_promoter_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/E11_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E11_promoter_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/E12_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E12_promoter_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/E13_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E13_promoter_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/E14_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E14_promoter_overlap_50bpsummits.bed
+bedtools slop -i promoters/clustered/E15_promoter_overlap_summits.bed -b 25 -g mm10.chrom.sizes > promoters/clustered/E15_promoter_overlap_50bpsummits.bed
+
+
+###################################################################################################
+################################### LIFTOVER PEAK SUMMITS ########################################
+###################################################################################################
+
+#### COMBINED PEAKS - ACTIVE IN BOTH DUNNART AND MOUSE ####
+
+# LiftOver dunnart peaks to mouse genome
+liftOver enhancers/enhancer_overlap_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/enhancer_summit50bp_smiCraTOmm10.bed enhancers/enhancer_summit50bp_smiCraTOmm10_unmapped.bed
+liftOver promoters/clustered/promoter_overlap_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed promoters/clustered/promoter_summit50bp_smiCraTOmm10_unmapped.bed
+
+## Lift back to the dunnart genome
+liftOver enhancers/enhancer_summit50bp_smiCraTOmm10.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/enhancer_summit50bp_smiCraTOmm10_mm10TOsmiCra.bed enhancers/enhancer_summit50bp_smiCraTOmm10_mm10TOsmiCra_unmapped.bed
+# 3820
+liftOver promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/promoter_summit50bp_smiCraTOmm10_mm10TOsmiCra.bed promoters/clustered/promoter_summit50bp_smiCraTOmm10_mm10TOsmiCra_unmapped.bed
+# 1881
+
+## Intersect dunnart CREs with mouse CREs.
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak -wo > promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E11.5.clustered.narrowPeak -wo > promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E12.5.clustered.narrowPeak -wo > promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E13.5.clustered.narrowPeak -wo > promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E14.5.clustered.narrowPeak -wo > promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E15.5.clustered.narrowPeak -wo > promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits.bed
+
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E10.5_enhancer_peaks.narrowPeak -wo > E10.5_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E11.5_enhancer_peaks.narrowPeak -wo > E11.5_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E12.5_enhancer_peaks.narrowPeak -wo > E12.5_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E13.5_enhancer_peaks.narrowPeak -wo > E13.5_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E14.5_enhancer_peaks.narrowPeak -wo > E14.5_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b E15.5_enhancer_peaks.narrowPeak -wo > E15.5_enhancerOverlapmm10_50bpsummits.bed
+
+## Window allows intersections within 50 bps. 
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak  > promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E11.5.clustered.narrowPeak  > promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E12.5.clustered.narrowPeak  > promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E13.5.clustered.narrowPeak  > promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E14.5.clustered.narrowPeak  > promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E15.5.clustered.narrowPeak  > promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_window.bed
+
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E10.5_enhancer_peaks.narrowPeak  > enhancers/E10.5_enhancerOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E11.5_enhancer_peaks.narrowPeak  > enhancers/E11.5_enhancerOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E12.5_enhancer_peaks.narrowPeak  > enhancers/E12.5_enhancerOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E13.5_enhancer_peaks.narrowPeak  > enhancers/E13.5_enhancerOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E14.5_enhancer_peaks.narrowPeak  > enhancers/E14.5_enhancerOverlapmm10_50bpsummits_window.bed
+bedtools window -w 50 -u -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E15.5_enhancer_peaks.narrowPeak  > enhancers/E15.5_enhancerOverlapmm10_50bpsummits_window.bed
+
+
+
+## Lift dunnart peaks back over to the dunnart genome
+liftOver -bedPlus=4 promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/E10.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E11.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E12.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E13.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E14.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E15.5_enhancerOverlapmm10_50bpsummits_window.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed
+
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+awk 'x[$4]++ == 1 { print $4 " is duplicated"}' E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed
+
+#### DUNNART SPECIFIC PEAKS - ACTIVE ONLY IN THE DUNNART ####
+## Intersect dunnart CREs with mouse CREs keeping those that DO NOT overlap.
+
+## Intersect all mouse stages
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak promoters/clustered/E11.5.clustered.narrowPeak promoters/clustered/E12.5.clustered.narrowPeak promoters/clustered/E13.5.clustered.narrowPeak promoters/clustered/E14.5.clustered.narrowPeak promoters/clustered/E15.5.clustered.narrowPeak -v > promoters/clustered/dunnart.specific_mm10_50bpsummits.bed
+
+## Dunnart specific at various stages
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E10.5.clustered.narrowPeak -v > promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E11.5.clustered.narrowPeak -v > promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E12.5.clustered.narrowPeak -v > promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E13.5.clustered.narrowPeak -v > promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E14.5.clustered.narrowPeak -v > promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a promoters/clustered/promoter_summit50bp_smiCraTOmm10.bed -b promoters/clustered/E15.5.clustered.narrowPeak -v > promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits.bed
+
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E10.5_enhancer_peaks.narrowPeak enhancers/E11.5_enhancer_peaks.narrowPeak enhancers/E12.5_enhancer_peaks.narrowPeak enhancers/E13.5_enhancer_peaks.narrowPeak enhancers/E14.5_enhancer_peaks.narrowPeak enhancers/E15.5_enhancer_peaks.narrowPeak -v > enhancers/dunnart.enhancer_specific_mm10_50bpsummits.bed
+
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E10.5_enhancer_peaks.narrowPeak -v > enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E11.5_enhancer_peaks.narrowPeak -v > enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E12.5_enhancer_peaks.narrowPeak -v > enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E13.5_enhancer_peaks.narrowPeak -v > enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E14.5_enhancer_peaks.narrowPeak -v > enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+bedtools intersect -a enhancers/enhancer_summit50bp_smiCraTOmm10.bed -b enhancers/E15.5_enhancer_peaks.narrowPeak -v > enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits.bed
+
+## Lift dunnart-specific peaks back over to the dunnart genome
+liftOver -bedPlus=4 promoters/clustered/dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+liftOver -bedPlus=4 promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/dunnart.enhancer_specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/dunnart.enhancer_specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/dunnart.enhancer_specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+
+#### MOUSE SPECIFIC PEAKS - ACTIVE ONLY IN THE MOUSE ####
+
+
+# LiftOver mouse peaks to dunnart genome
+liftOver enhancers/E10_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E10_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver enhancers/E11_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E11_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver enhancers/E12_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E12_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver enhancers/E13_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E13_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver enhancers/E14_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E14_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver enhancers/E15_enhancer_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15_enhancer_summit50bp_mm10TOsmiCra.bed enhancers/E15_enhancer_summit50bp_mm10TOsmiCra_unmapped.bed
+
+liftOver promoters/clustered/E10_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver promoters/clustered/E11_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver promoters/clustered/E12_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver promoters/clustered/E13_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver promoters/clustered/E14_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+liftOver promoters/clustered/E15_promoter_overlap_50bpsummits.bed ../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra.bed promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra_unmapped.bed
+
+## Intersect dunnart CREs with mouse CREs keeping those that DO NOT overlap.
+
+bedtools intersect -a promoters/clustered/E10_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a promoters/clustered/E11_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a promoters/clustered/E12_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a promoters/clustered/E13_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a promoters/clustered/E14_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a promoters/clustered/E15_promoter_summit50bp_mm10TOsmiCra.bed -b promoters/clustered/dunnart.clustered.narrowPeak -v > promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits.bed
+
+bedtools intersect -a enhancers/E10_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a enhancers/E11_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a enhancers/E12_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a enhancers/E13_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a enhancers/E14_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+bedtools intersect -a enhancers/E15_enhancer_summit50bp_mm10TOsmiCra.bed -b enhancers/dunnart_enhancer_peaks.narrowPeak -v > enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits.bed
+
+## Lift mouse-specific peaks back over to the mouse genome
+liftOver -bedPlus=4 promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E10.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E11.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E12.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E13.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E14.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10.bed promoters/clustered/E15.5_promoter_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E10.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E11.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E12.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E13.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E14.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10.bed enhancers/E15.5_enhancer_mouse.specific_smiCra_50bpsummits_backTOmm10_unmapped.bed
+
+## Lift dunnart-specific peaks and combined peaks back into mm10 coordinates for GREAT analysis
+liftOver -bedPlus=4 promoters/clustered/dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+liftOver -bedPlus=4 promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E10.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E11.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E12.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E13.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E14.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E15.5.clustered_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/dunnart.enhancer_specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/dunnart.enhancer_specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/dunnart.enhancer_specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E10.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E11.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E12.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E13.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E14.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E15.5_enhancer_dunnart.specific_mm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+## Combined peaks
+
+liftOver -bedPlus=4 promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E10.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E11.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E12.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E13.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E14.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed promoters/clustered/E15.5_promoterOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+liftOver -bedPlus=4 enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E10.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E11.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E12.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E13.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E14.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+liftOver -bedPlus=4 enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed ../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10.bed enhancers/E15.5_enhancerOverlapmm10_50bpsummits_backTOsmiCra_backTOmm10_unmapped.bed
+
+
+###################################################################################################
+###################################### MERGE MOUSE PEAKS ##########################################
+###################################################################################################
+## PROMOTERS
+## Concatenate mouse narrowPeak files, sort and then merge
+cat E10.5.clustered.narrowPeak E11.5.clustered.narrowPeak E12.5.clustered.narrowPeak E13.5.clustered.narrowPeak E14.5.clustered.narrowPeak E15.5.clustered.narrowPeak > mouse.clustered.narrowPeak
+sort -k1,1 -k2,2n mouse.clustered.narrowPeak > mouse.clustered.sorted.narrowPeak
+bedtools merge -d 10 -i mouse.clustered.sorted.narrowPeak > mouse.clustered.sorted.merged.narrowPeak
+
+bedtools intersect -a promoter_summit50bp_smiCraTOmm10.bed -b mouse.clustered.sorted.merged.narrowPeak -wo > mouse_merged_promoterOverlapmm10_50bpsummits.bed
+bedtools intersect -a promoter_summit50bp_smiCraTOmm10.bed -b mouse.clustered.sorted.merged.narrowPeak -v > dunnart_specific_mouse_merged_promoterOverlapmm10_50bpsummits.bed
+
+## Liftover back to dunnart to keep only peaks that can be reciprocally found
+liftOver -bedPlus=4 mouse_merged_promoterOverlapmm10_50bpsummits.bed ../../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 dunnart_specific_mouse_merged_promoterOverlapmm10_50bpsummits.bed ../../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart_specific_mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra.bed dunnart_specific_mouse_merged_promoterOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+## Find mouse specific peaks 
+## Concatenate mouse 50bp summit narrowPeak files, sort, merge then liftOver to dunnart to find mouse specific peaks
+cat E10_promoter_overlap_50bpsummits.bed E11_promoter_overlap_50bpsummits.bed E12_promoter_overlap_50bpsummits.bed E13_promoter_overlap_50bpsummits.bed E14_promoter_overlap_50bpsummits.bed E15_promoter_overlap_50bpsummits.bed > mouse_promoter_overlap_50bpsummits.bed
+sort -k1,1 -k2,2n mouse_promoter_overlap_50bpsummits.bed > mouse_promoter_overlap_50bpsummits.sorted.bed
+bedtools merge -d 10 -i mouse_promoter_overlap_50bpsummits.sorted.bed > mouse_promoter_overlap_50bpsummits.sorted.merged.bed
+
+## LiftOver mouse 50bp summits to the dunnart coordinates
+liftOver -bedPlus=4 mouse_promoter_overlap_50bpsummits.sorted.merged.bed ../../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed mouse_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_unmapped.bed
+## Intersect keeping only those that DO NOT overlap with the dunnart
+bedtools intersect -a mouse_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed -b dunnart.clustered.narrowPeak -v > mouse_specific_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed
+## Keep only those that can be reciprocally mapped back to the mouse genome
+liftOver -bedPlus=4 mouse_specific_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed ../../../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_specific_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10.bed mouse_specific_promoter_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10_unmapped.bed
+
+## ENHANCERS
+## Concatenate mouse narrowPeak files, sort and then merge
+cat E10.5_enhancer_peaks.narrowPeak E11.5_enhancer_peaks.narrowPeak E12.5_enhancer_peaks.narrowPeak E13.5_enhancer_peaks.narrowPeak E14.5_enhancer_peaks.narrowPeak E15.5_enhancer_peaks.narrowPeak > mouse_enhancers.narrowPeak
+sort -k1,1 -k2,2n mouse_enhancers.narrowPeak > mouse_enhancers.sorted.narrowPeak
+bedtools merge -d 10 -i mouse_enhancers.sorted.narrowPeak > mouse_enhancers.sorted.merged.narrowPeak
+
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b mouse_enhancers.sorted.merged.narrowPeak -wo > mouse_merged_enhancerOverlapmm10_50bpsummits.bed
+bedtools intersect -a enhancer_summit50bp_smiCraTOmm10.bed -b mouse_enhancers.sorted.merged.narrowPeak -v > dunnart_specific_mouse_merged_enhancerOverlapmm10_50bpsummits.bed
+
+## Liftover back to dunnart to keep only peaks that can be reciprocally found
+liftOver -bedPlus=4 mouse_merged_enhancerOverlapmm10_50bpsummits.bed ../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+liftOver -bedPlus=4 dunnart_specific_mouse_merged_enhancerOverlapmm10_50bpsummits.bed ../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain dunnart_specific_mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra.bed dunnart_specific_mouse_merged_enhancerOverlapmm10_50bpsummits_backTOsmiCra_unmapped.bed
+
+## Find mouse specific peaks 
+## Concatenate mouse 50bp summit narrowPeak files, sort, merge then liftOver to dunnart to find mouse specific peaks
+cat E10_enhancer_overlap_50bpsummits.bed E11_enhancer_overlap_50bpsummits.bed E12_enhancer_overlap_50bpsummits.bed E13_enhancer_overlap_50bpsummits.bed E14_enhancer_overlap_50bpsummits.bed E15_enhancer_overlap_50bpsummits.bed > mouse_enhancer_overlap_50bpsummits.bed
+sort -k1,1 -k2,2n mouse_enhancer_overlap_50bpsummits.bed > mouse_enhancer_overlap_50bpsummits.sorted.bed
+bedtools merge -d 10 -i mouse_enhancer_overlap_50bpsummits.sorted.bed > mouse_enhancer_overlap_50bpsummits.sorted.merged.bed
+
+## LiftOver mouse 50bp summits to the dunnart coordinates
+liftOver -bedPlus=4 mouse_enhancer_overlap_50bpsummits.sorted.merged.bed ../../mm10TOsmiCra1_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed mouse_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_unmapped.bed
+## Intersect keeping only those that DO NOT overlap with the dunnart
+bedtools intersect -a mouse_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed -b dunnart_enhancer_peaks.narrowPeak -v > mouse_specific_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed
+## Keep only those that can be reciprocally mapped back to the mouse genome
+liftOver -bedPlus=4 mouse_specific_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra.bed ../../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain mouse_specific_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10.bed mouse_specific_enhancer_overlap_50bpsummits.sorted.merged_mm10TOsmiCra_backTOmm10_unmapped.bed
+
+### IGR: When you lift over a peak from one species to another, does it always land next to the same gene in both species?
+## Use annotated peak files for all peaks and compare nearest gene call
+## Intersect annotation file with peak summits
+
+bedtools intersect -a promoter_overlap_50bpsummits.bed -b cluster1_promoters_annotationConvertedIDs.bed -wo > dunnart_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E10_promoter_overlap_50bpsummits.bed -b clustered.E10.filtered_annotation.bed -wo > E10_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E11_promoter_overlap_50bpsummits.bed -b clustered.E11.filtered_annotation.bed -wo > E11_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E12_promoter_overlap_50bpsummits.bed -b clustered.E12.filtered_annotation.bed -wo > E12_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E13_promoter_overlap_50bpsummits.bed -b clustered.E13.filtered_annotation.bed -wo > E13_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E14_promoter_overlap_50bpsummits.bed -b clustered.E14.filtered_annotation.bed -wo > E14_promoter_50bpsummits_annotation.bed
+bedtools intersect -a E15_promoter_overlap_50bpsummits.bed -b clustered.E15.filtered_annotation.bed -wo > E15_promoter_50bpsummits_annotation.bed
+
+liftOver -bedPlus=3 dunnart_promoter_50bpsummits_annotation.bed ../../../smiCra1TOmm10_patched_sorted_repFil_chainCl_preNet_chainNet_stitched.chain promoter_overlap_50bpsummits_annotation_smiCraTOmm10.bed promoter_overlap_50bpsummits_annotation_smiCraTOmm10_unmapped.bed
+
+bedtools intersect -a promoter_overlap_50bpsummits_annotation_smiCraTOmm10.bed -b clustered.E10.filtered_annotation.bed -wo > promoter_mm10intersect_50bpsummits_annotation_smiCraTOmm10.bed
\ No newline at end of file
diff --git a/compare_liftover_genes.R b/compare_liftover_genes.R
new file mode 100644
index 0000000000000000000000000000000000000000..411c84bdc01235cf7042a7a4a1304161f062ac97
--- /dev/null
+++ b/compare_liftover_genes.R
@@ -0,0 +1,14 @@
+
+## Load packages
+library(data.table)
+library(tidyverse)
+
+bedList("")
+bedFiles = list.files(pattern= "dunnart_mouse", full.names=T) # create list of files in directory
+
+files = as.list(bedFiles)
+data = lapply(files, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+data2 = rbindlist(data,)
+data2$score <- ifelse(data2$V27 == data2$V46, "1", "0")
+split = data2 %>% split(by="V49") ## split by stage
+counts = lapply(split, function(x) length(which(x$score !=0)))
\ No newline at end of file
diff --git a/cross_species_comparison/README.md b/cross_species_comparison/README.md
deleted file mode 100644
index a3ea1641bad0b680abe37506b1b684d349b965f3..0000000000000000000000000000000000000000
--- a/cross_species_comparison/README.md
+++ /dev/null
@@ -1,300 +0,0 @@
-# Cross-species ChIPseq analysis
-
-## Whole Genome Alignment
-
-__Email from Camille Berthelot:__
-
-We have run into a similar problem with non-model species in a recent project.
-Here is the pipeline that we have had good success with, which is very similar to the conservation pipeline used by UCSC:
-
-- build a custom whole-genome alignment between the dunnart and mouse genomes with Lastz
-- produce the chains and nets for this alignment (e.g. hierarchical organisation of aligned regions to get a 1-to-1 alignment over the whole genomes)
-- use LiftOver to map your dunnart regions of interest to mouse (and reciprocally)
-
-We have benchmarked this pipeline against the one we used in the 20 mammals Cell paper, and there was almost no difference in the recovered regions.
-
-This however assumes that you have a good grip on bioinformatics and access to sufficient computational power, as building alignments is quite intensive. There are a number of online tutorials to build the alignment and nets using Lastz:
-http://genomewiki.ucsc.edu/index.php/Whole_genome_alignment_howto
-https://www.bioconductor.org/packages/release/bioc/vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html
-https://github.com/hillerlab/GenomeAlignmentTools (more fine-tuned and advanced)
-
-__Methods from Hecker & Hiller 2020 Whole Genome Alignment:__
-Going to try this method.
-
-To compute pairwise and multiple genome alignments, we used the human hg38 assembly as the reference (Supplementary Fig. 1 shows the entire workflow). We first built pairwise alignments between human and a query species using lastz and axtChain to compute co-linear alignment chains [82]. To align placental mammals, we used previously determined lastz parameters (K = 2400, L = 3000, Y = 9400, H = 2000, and the lastz default scoring matrix) that have a sufficient sensitivity to capture orthologous exons [16]. To align chimpanzee, bonobo, and gorilla, we changed the lastz parameters (K = 4500 and L = 4500).
-
-After building chains, we applied RepeatFiller (RRID:SCR_017414), a method that performs another round of local alignment, considering unaligning regions ≤20 kb in size that are bounded by co-linear alignment blocks up- and downstream. RepeatFiller removes any repeat masking from the unaligned region and is therefore able to detect novel alignments between repetitive regions. We have previously shown that RepeatFiller detects several megabases of aligning repetitive sequences that would be missed otherwise. After RepeatFiller, we applied chainCleaner with parameters -LRfoldThreshold = 2.5 -doPairs -LRfoldThresholdPairs = 10 -maxPairDistance = 10000 -maxSuspectScore = 100000 -minBrokenChainScore = 75000 to improve alignment specificity. Pairwise alignment chains were converted into alignment nets using a modified version of chainNet that computes real scores of partial nets. Nets were filtered using NetFilterNonNested.perl with parameters -doUCSCSynFilter -keepSynNetsWithScore 5000 -keepInvNetsWithScore 5000, which applies the UCSC “syntenic net” score thresholds (minTopScore of 300000 and minSynScore of 200000) and keeps nested nets that align to the same locus (inversions or local translocations; net type “inv” or “syn” according to netClass) if they score ≥5,000. For the Mongolian gerbil, tarsier, Malayan flying lemur, sperm whale, Przewalski's horse, Weddell seal, Malayan pangolin, Chinese pangolin, Hoffmann's two-fingered sloth, and Cape rock hyrax that have genome assemblies with a scaffold N50 ≤1,000,000 and a contig N50 ≤100,000, we just required that nets have a score ≥100,000. For marsupials and platypus, we lowered the score threshold for nets to 10,000 and kept inv or syn nets with scores ≥3,000.
-
-### Some definitions
-
-In chain and net lingo, the __target__ is the reference genome sequence and the __query__ is some other genome sequence. For example, if you are viewing Human-Mouse alignments in the Human genome browser, human is the target and mouse is the query.
-
-A __gapless block__ is a base-for-base alignment between part of the target and part of the query, possibly including mismatching bases. It has the same length in bases on the target and the query. This is the output of the most primitive alignment algorithms.
-
-A __gap__ is a link between two gapless blocks, indicating that the target or the query has sequence that should be skipped over in order to make the best-scoring alignment. In other words, the scoring penalty for skipping over one or more bases is less than the penalty for continuing to align the sequences without skipping.
-
-A __single-sided gap__ is a gap in which sequence in either target or query must be skipped over. A plausible explanation for needing to skip over a base in the target while not skipping a base in the query is that either the target has an inserted base or the query has a deleted base. Many alignment tools produce alignments with single-sided gaps between gapless blocks.
-
-A __double-sided gap__ skips over sequence in both target and query because the sum of penalties for mismatching bases exceeds the penalty for extending a gap across them. This is possible only when the penalty for extending a gap is less than the penalty for creating a new gap and less than the penalty for a mismatch, and when the alignment algorithm is capable of considering double-sided gaps.
-
-A __chain__ is a sequence of non-overlapping gapless blocks, with single- or double-sided gaps between blocks. Within a chain, target and query coords are monotonically non-decreasing (i.e. always increasing or flat). Chains are constructed by the axtChain program which finds pairwise alignments with the same target and query sequence, on the same strand, that can be merged if overlapping and joined into one longer alignment with a higher score under an affine gap-scoring system (progressively decreasing penalties for longer gaps).
-
-* double-sided gaps are a new capability (blastz can't do that) that allow extremely long chains to be constructed.
-* not just orthologs, but paralogs too, can result in good chains. but that's useful!
-* chains should be symmetrical -- e.g. swap human-mouse -> mouse-human chains, and you should get approx. the same chains as if you chain swapped mouse-human blastz alignments. However, Blastz's dynamic masking is asymmetrical, so in practice those results are not exactly symmetrical. Also, dynamic masking in conjunction with changed chunk sizes can cause differences in results from one run to the next.
-* chained blastz alignments are not single-coverage in either target or query unless some subsequent filtering (like netting) is done.
-* chain tracks can contain massive pileups when a piece of the target aligns well to many places in the query. Common causes of this include insufficient masking of repeats and high-copy-number genes (or paralogs).
-
-A __net__ is a hierarchical collection of chains, with the highest-scoring non-overlapping chains on top, and their gaps filled in where possible by lower-scoring chains, which in turn may have gaps filled in by lower-level chains and so on.
-
-* I think a chain's qName also helps to determine which level it lands in, i.e. it makes a difference whether a chain's qName is the same as the top-level chain's qName or not, because the levels have meanings associated with them -- see details page.
-* a net is single-coverage for target but not for query, unless it has been filtered to be single-coverage on both target and query. By convention we add "rbest" to the net filename in that case.
-* because it's single-coverage in the target, it's no longer symmetrical.
-* the netter has two outputs, one of which we usually ignore: the target-centric net in query coordinates. The reciprocal best process uses that output: the query-referenced (but target-centric / target single-cov) net is turned back into component chains, and then those are netted to get single coverage in the query too; the two outputs of that netting are reciprocal-best in query and target coords. Reciprocal-best nets are symmetrical again.
-* nets do a good job of filtering out massive pileups by collapsing them down to (usually) a single level.
-* "LiftOver chains" are actually chains extracted from nets, or chains filtered by the netting process.
-
-
-### Preparation
-
-Spartan modules
-
-```
-module load foss
-module load lastz
-module load ucsc/21072020
-module load perl
-
-```
-
-#### Repeat mask dunnart genome
-
-Create conda environment will all the dependencies:
-
-```
-conda create -n wga
-conda activate wga
-conda config --add channels conda-forge
-conda config --add channels biocore
-conda config --add channels bioconda
-conda install repeatmasker
-```
-
-Run RepeatModeler to de novo find repeat regions in the dunnart genome:
-```
-BuildDatabase -name dunnart -engine ncbi Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fasta
-
-nohup RepeatModeler -database dunnart -pa 20 >& repeatmodeler.out
-
-```
-
-Run RepeatMasker to mask repeats in dunnart genome (makes repeats lowercase). Run as an array for scaffolds to make it quicker.
-Create commands for array slurm script: `repeatMasker.sh`
-
-```
-RepeatMasker -q -xsmall Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fasta -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-```
-
-#### Split into scaffolds
-
-Using faSplit from the UCSC Kent Tools
-
-```
-faSplit byName Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fasta smiCra1/
-```
-
-
-#### Create .2bit and .sizes files
-
-```
-faToTwoBit ../../dunnart/genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr_RM.fasta smiCra1.2bit
-```
-
-```
-twoBitInfo smiCra1.2bit stdout | sort -k2rn > smiCra1.chrom.sizes
-```
-
-#### mm10 target genome
-http://hgdownload.cse.ucsc.edu/goldenpath/mm10/bigZips/mm10.2bit
-
-__mm10.2bit__ - contains the complete mouse/mm10 genome sequence in the 2bit file format.  Repeats from __RepeatMasker__ and __Tandem Repeats Finder__ (with period of 12 or less) are shown in lower case; non-repeating sequence is shown in upper case.  
-
-
-### lastZ
-
-To align placental mammals, we used previously determined lastz parameters (K = 2400, L = 3000, Y = 9400, H = 2000, and the lastz default scoring matrix) that have a sufficient sensitivity to capture orthologous exons
-
-To align placental mammals, we used the lastz alignment parameters K = 2400, L = 3000, Y = 9400, H = 2000 and the lastz default scoring matrix, correspond- ing to parameter set 2 in Table 1. To align vertebrates, we used K = 2400, L = 3000, Y = 3400, H = 2000 and the HoxD55 scoring matrix. Citation: Increased alignment sensitivity improves the usage of genome alignments for comparative gene annotation. Nucleic Acids Res. 2017;45(14):8369–77.
-
-Create commands for running lastZ for all scaffolds: `lastz.sh`
-
-
-Run as an array on slurm: `array_wrapper.slurm`
-
-
-#### Convert maf to axt-format
-http://last.cbrc.jp/doc/maf-convert.html
-
-```
-module load last/last/1066
-maf-convert axt ${tr}.maf > ${tr}.axt
-```
-
-
-### axtChain
-
-We use axtChain (http://www.soe.ucsc.edu/~kent; default parameters) to build co-linear alignment chains.
-
-```
-axtChain -linearGap=loose mm10_smiCra1.axt mm10.2bit smiCra1.2bit mm10_smiCra1.chain
-
-```
-
-### chainMergeSort
-Merge short chains into longer ones, concatenate chains and sort
-
-```
-chainMergeSort *.chain > smiCra1_mm10.chain
-```
-
-Installing Genomic Alignment Tools (Hiller group)
-
-```
-git clone https://github.com/hillerlab/GenomeAlignmentTools.git
-
-cd GenomeAlignmentTools/kent/src
-
-make
-
-# Kent binaries
-PATH=$PATH:/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/kent/bin;export PATH
-
-export KENTSRC_DIR=/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/kent/src/
-
-cd /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/src
-export MACHTYPE=x86_64
-make 
-
-PATH=$PATH:/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/src
-
-```
-
-
-
-### RepeatFiller
-https://github.com/hillerlab/GenomeAlignmentTools
-RepeatFiller [5] is a tool to incorporate newly-detected repeat-overlapping alignments into pairwise alignment chains [4]. Its runtime adds little to the computationally more expensive step of generating chains in pairwise whole-genome alignments. RepeatFiller circumvents the problem that considering all repeat-overlapping alignment seeds during whole genome alignment is computationally not feasible. Therefore, RepeatFiller only aligns local genomic regions that are bounded by colinear aligning blocks, as provided in the chains, which makes it feasible to consider all seeds including those that overlap repetitive regions. RepeatFiller application to mammalian genome alignment chains can add between 22 and 84 Mb of previously-undetected alignments that mostly originate from transposable elements [5]. This helps to comprehensively align repetitive regions and improves the annotation of conserved non-coding elements.
-
-```
-python3 RepeatFiller.py -c smiCra1_mm10.chain -T2 mm10.2bit -Q2 smiCra1.2bit
-```
-
-### patchChain
-patchChain.perl performs a highly sensitive local pairwise alignment for loci flanked by aligning blocks [1,3]. Given an alignment chain [4], it considers all chains that pass the score and span filters (optional parameters), extracts all the unaligning loci and creates local alignment jobs. After executing these alignment jobs, the newly found and the original local alignments are combined and used to produce a new set of improved chains.
-
-This procedure is recommended for comparisons between species that are separated by >0.75 substitutions per neutral site [1].
-
-
-```
-patchChain.perl /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/wholeGenomeAlignment/chainCleaner/smiCra1_mm10_repFill_chainCl.chain /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.chrom.sizes /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.chrom.sizes -chainMinScore 5000 -gapMaxSizeT 500000 -gapMaxSizeQ 500000 -gapMinSizeT 30 -gapMinSizeQ 30 -numJobs 10 -jobDir jobs -jobList jobList -outputDir pslOutput -minEntropy 1.8 -windowSize 30 -minIdentity 60 -lastzParameters "--format=axt K=1500 L=2500 M=0 T=0 W=5 Q=/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/example/HoxD55.q"
-
-```
-
-This create a folder with the jobs and a jobList which can be called in an array slurm script. Then you can run the jobs in parallel.
-Need to work out how to combine these and do I then run chainCleaner again?
-
-
-
-### chainCleaner
-https://github.com/hillerlab/GenomeAlignmentTools
-After RepeatFiller, we applied chainCleaner with parameters -LRfoldThreshold = 2.5 -doPairs -LRfoldThresholdPairs = 10 -maxPairDistance = 10000 -maxSuspectScore = 100000 -minBrokenChainScore = 75000 to improve alignment specificity.
-
-chainCleaner improves the specificity in genome alignment chains by detecting and removing local alignments that obscure the evolutionary history of genomic rearrangements [2]. The input is a chain file, ideally after adding alignments found with highly sensitive parameters if distal species are compared. The output is a chain file that contains re-scored and score-sorted chains after removing the local alignments from the parent chains and adding them as individual chains. The resulting output file can be used to get alignment nets by running chainNet [4].
-
-
-```
-chainCleaner smiCra1_mm10_repFil.chain mm10.2bit smiCra1.2bit smiCra1_mm10_repFill_chainCl.chain smiCra1_mm10_repFill_chainCl.bed -tSizes=mm10.chrom.sizes -qSizes=smiCra1.chrom.sizes -LRfoldThreshold=2.5 -doPairs -LRfoldThresholdPairs=10 -maxPairDistance=10000 -maxSuspectScore=100000 -minBrokenChainScore=75000 -linearGap=loose
-```
-
-`-LRfoldThreshold` = threshold for removing local alignment blocks if the score of the left and right fill of brokenChain. Default is 2.5
-`-doPairs` = flag: if set, do test if pairs of chain breaking alignments can be removed
-`-LRfoldThresholdPairs` = threshold for removing local alignment blocks if the score of the left and right fill of broken chains (for pairs). Default = 10
-`-maxPairDistance` = only consider pairs of chain breaking alignments where the distance between the end of the upstream CBA and the start of the downstream CBA is at most that many bp (default 10000)
-`-maxSuspectScore` = threshold for score of suspect subChain. If higher, do not remove suspect.
-
-`-linearGap`=loose
-
-### chainNet
-Given a set of alignment chains, chainNet produces alignment nets, which is a hierarchical collection of chains or parts of chains that attempt to capture only orthologous alignments [4]. The original chainNet implementation approximates the score of "sub-nets" (nets that come from a part of a chain and fill a gap in a higher-level net) by the fraction of aligning bases. This can lead to a bias in case the aligning blocks of a chain are not equally distributed. We implemented a new parameter "-rescore" in chainNet that computes the real score of each subnet [2].
-
-Make the alignment nets:
-
-```
-chainNet /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/wholeGenomeAlignment/chainCleaner/smiCra1_mm10_repFill_chainCl.chain /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.chrom.sizes /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.chrom.sizes /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/wholeGenomeAlignment/chainNet/mm10.net /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/wholeGenomeAlignment/chainNet/smiCra1.net 
-```
-
-### netClass
-Add classification info to net.
-usage:
-   netClass [options] in.net tDb qDb out.net
-       tDb - database to fetch target repeat masker table information
-       qDb - database to fetch query repeat masker table information
-
-```
-netClass chainNet/mm10.net ../data/genomes/mm10.db ../data/genomes/smiCra1.db mm10_class.net
-can't find database ../data/genomes/mm10.db in hg.conf, should have a default named "db"
-```
-ERROR with netClass, not sure where to get the "database" from??
-
-
-
-### NetFilterNonNested
-Before building a multiple alignment from the pairwise alignment nets, it is recommended to remove low-scoring alignment nets that are unlikely to represent real homologies. While the netFilter program [4] removes nested nets in case their parent net does not satisfy the specified score and size criteria, NetFilterNonNested.perl applies a non-nested filtering procedure that considers and filters each net individually [1,3]. This avoids removing nested nets that would satisfy the specified criteria, even if a parent net is removed.
-Keeping nets that score higher than 10000 and keeping all nested nets that align to the same locus if they score higher than 3000 for non-placental mammals NetFilterNonNested.perl -doScoreFilter -keepSynNetsWithScore 3000 -keepInvNetsWithScore 3000 -minScore1 10000 ref.query.net.gz > ref.query.filtered.net
-
-NetFilterNonNested.perl -doScoreFilter -keepSynNetsWithScore 3000 -keepInvNetsWithScore 3000 -minScore1 10000 mm10.smiCra1.net > mm10.smiCra1.filtered.net
-
-NetFilterNonNested.perl -doScoreFilter -minScore1 10000 -keepSynNetsWithScore 3000 -keepInvNetsWithScore 3000
-
-
-### netChainSubset
-Create liftOver chain
-
-```
-netChainSubset ../net/chr.net chr.chain ../over/netchr.chain
-```
-
-### LiftOver
-
-dunnart_to_mouse
-
-liftOver -bedPlus=4 ../../mouse/results/macs2/H3K27ac_E11.5_overlap.narrowPeak netChainSubset/smiCra1_mm10.chain test.bed unmapped.bed
-
-
-### Quality control 
-FluentDNA looks pretty good for comparing the quality of the alignment. Gives a table of statistics but also provides visualisation tools which looks cool!
-
-https://github.com/josiahseaman/FluentDNA
-
-```
-./fluentdna --fasta=data/hg38.fa --chainfile=data/hg38ToPanTro5.over.chain --extrafastas data/panTro5.fa --chromosomes chr19 --outname="Human vs Chimpanzee"
-```
-
-
-
-
-### References
-
-[1] Sharma V, Hiller M. Increased alignment sensitivity improves the usage of genome alignments for comparative gene annotation. Nucleic Acids Res., 45(14), 8369–8377, 2017
-
-[2] Suarez H, Langer BE, Ladde P, Hiller M. chainCleaner improves genome alignment specificity and sensitivity. Bioinformatics, 33(11):1596-1603, 2017
-
-[3] Hiller M, Agarwal S, Notwell JH, Parikh R, Guturu H, Wenger AM, Bejerano G. Computational methods to detect conserved non-genic elements in phylogenetically isolated genomes: application to zebrafish. Nucleic Acids Res, 41(15):e151.
-
-[4] Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. PNAS, 100(20):11484-9, 2003
-
-[5] Osipova E, Hecker N, Hiller M. RepeatFiller newly identifies megabases of aligning repetitive sequences and improves annotations of conserved non-exonic elements, submitted
-
-[6] FluentDNA paper
-
-[7] Kent tools UCSC
\ No newline at end of file
diff --git a/cross_species_comparison/env/wga.yaml b/cross_species_comparison/env/wga.yaml
deleted file mode 100644
index 96fbe7144bb502e900a78fb38048912802f4f16b..0000000000000000000000000000000000000000
--- a/cross_species_comparison/env/wga.yaml
+++ /dev/null
@@ -1,153 +0,0 @@
-name: wga
-channels:
-  - bioconda
-  - biocore
-  - conda-forge
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=conda_forge
-  - _openmp_mutex=4.5=1_gnu
-  - bzip2=1.0.8=h516909a_3
-  - c-ares=1.11.0=h470a237_1
-  - ca-certificates=2020.6.20=hecda079_0
-  - cairo=1.16.0=h3fc0475_1005
-  - cd-hit=4.8.1=h8b12597_3
-  - certifi=2020.6.20=py38h32f6830_0
-  - curl=7.71.1=he644dc0_6
-  - entrez-direct=13.9=pl526h375a9b1_0
-  - expat=2.2.9=he1b5a44_2
-  - fontconfig=2.13.1=h1056068_1002
-  - freetype=2.10.2=he06d7ca_0
-  - fribidi=1.0.10=h516909a_0
-  - genometools-genometools=1.6.1=py38h23571c4_2
-  - gettext=0.19.8.1=hc5be6a0_1002
-  - glib=2.66.0=h0dae87d_0
-  - gmp=6.1.2=hf484d3e_1000
-  - gnutls=3.5.19=h2a4e5f8_1
-  - graphite2=1.3.13=he1b5a44_1001
-  - harfbuzz=2.7.2=hee91db6_0
-  - hmmer=3.3.1=he1b5a44_0
-  - icu=67.1=he1b5a44_0
-  - krb5=1.17.1=hfafb76e_3
-  - ld_impl_linux-64=2.35=h769bd43_9
-  - libcurl=7.71.1=hcdd3856_6
-  - libedit=3.1.20191231=he28a2e2_2
-  - libev=4.33=h516909a_1
-  - libffi=3.2.1=he1b5a44_1007
-  - libgcc-ng=9.3.0=h24d8f2e_16
-  - libgomp=9.3.0=h24d8f2e_16
-  - libiconv=1.16=h516909a_0
-  - libnghttp2=1.41.0=hab1572f_1
-  - libpng=1.6.37=hed695b0_2
-  - libssh2=1.9.0=hab1572f_5
-  - libstdcxx-ng=9.3.0=hdf63c60_16
-  - libuuid=2.32.1=h14c3975_1000
-  - libxcb=1.13=h14c3975_1002
-  - libxml2=2.9.10=h68273f3_2
-  - llvm-openmp=8.0.1=hc9558a2_0
-  - ltr_retriever=2.9.0=0
-  - mafft=7.471=h516909a_0
-  - ncurses=6.2=he1b5a44_1
-  - nettle=3.3=0
-  - nseg=1.0.1=h516909a_0
-  - openmp=8.0.1=0
-  - openssl=1.1.1h=h516909a_0
-  - pango=1.42.4=h7062337_4
-  - pcre=8.44=he1b5a44_0
-  - perl=5.26.2=h516909a_1006
-  - perl-app-cpanminus=1.7044=pl526_1
-  - perl-archive-tar=2.32=pl526_0
-  - perl-base=2.23=pl526_1
-  - perl-business-isbn=3.004=pl526_0
-  - perl-business-isbn-data=20140910.003=pl526_0
-  - perl-carp=1.38=pl526_3
-  - perl-common-sense=3.74=pl526_2
-  - perl-compress-raw-bzip2=2.087=pl526he1b5a44_0
-  - perl-compress-raw-zlib=2.087=pl526hc9558a2_0
-  - perl-constant=1.33=pl526_1
-  - perl-data-dumper=2.173=pl526_0
-  - perl-digest-hmac=1.03=pl526_3
-  - perl-digest-md5=2.55=pl526_0
-  - perl-encode=2.88=pl526_1
-  - perl-encode-locale=1.05=pl526_6
-  - perl-exporter=5.72=pl526_1
-  - perl-exporter-tiny=1.002001=pl526_0
-  - perl-extutils-makemaker=7.36=pl526_1
-  - perl-file-listing=6.04=pl526_1
-  - perl-file-path=2.16=pl526_0
-  - perl-file-temp=0.2304=pl526_2
-  - perl-file-which=1.23=pl526_0
-  - perl-html-parser=3.72=pl526h6bb024c_5
-  - perl-html-tagset=3.20=pl526_3
-  - perl-html-tree=5.07=pl526_1
-  - perl-http-cookies=6.04=pl526_0
-  - perl-http-daemon=6.01=pl526_1
-  - perl-http-date=6.02=pl526_3
-  - perl-http-message=6.18=pl526_0
-  - perl-http-negotiate=6.01=pl526_3
-  - perl-io-compress=2.087=pl526he1b5a44_0
-  - perl-io-html=1.001=pl526_2
-  - perl-io-socket-ssl=2.066=pl526_0
-  - perl-io-zlib=1.10=pl526_2
-  - perl-json=4.02=pl526_0
-  - perl-json-xs=2.34=pl526h6bb024c_3
-  - perl-libwww-perl=6.39=pl526_0
-  - perl-list-moreutils=0.428=pl526_1
-  - perl-list-moreutils-xs=0.428=pl526_0
-  - perl-lwp-mediatypes=6.04=pl526_0
-  - perl-lwp-protocol-https=6.07=pl526_4
-  - perl-mime-base64=3.15=pl526_1
-  - perl-mozilla-ca=20180117=pl526_1
-  - perl-net-http=6.19=pl526_0
-  - perl-net-ssleay=1.88=pl526h90d6eec_0
-  - perl-ntlm=1.09=pl526_4
-  - perl-parent=0.236=pl526_1
-  - perl-pathtools=3.75=pl526h14c3975_1
-  - perl-scalar-list-utils=1.52=pl526h516909a_0
-  - perl-socket=2.027=pl526_1
-  - perl-storable=3.15=pl526h14c3975_0
-  - perl-test-requiresinternet=0.05=pl526_0
-  - perl-text-soundex=3.05=pl526_1000
-  - perl-time-local=1.28=pl526_1
-  - perl-try-tiny=0.30=pl526_1
-  - perl-types-serialiser=1.0=pl526_2
-  - perl-uri=1.76=pl526_0
-  - perl-www-robotrules=6.02=pl526_3
-  - perl-xml-namespacesupport=1.12=pl526_0
-  - perl-xml-parser=2.44=pl526h4e0c4b3_7
-  - perl-xml-sax=1.02=pl526_0
-  - perl-xml-sax-base=1.09=pl526_0
-  - perl-xml-sax-expat=0.51=pl526_3
-  - perl-xml-simple=2.25=pl526_1
-  - perl-xsloader=0.24=pl526_0
-  - pip=20.2.3=py_0
-  - pixman=0.38.0=h516909a_1003
-  - pthread-stubs=0.4=h14c3975_1001
-  - python=3.8.5=h1103e12_8_cpython
-  - python_abi=3.8=1_cp38
-  - readline=8.0=he28a2e2_2
-  - recon=1.08=h516909a_2
-  - repeatmasker=4.1.0=pl526_0
-  - repeatmodeler=2.0.1=pl526_0
-  - repeatscout=1.0.6=h516909a_1
-  - rmblast=2.9.0=h2d02072_0
-  - setuptools=49.6.0=py38h32f6830_1
-  - sqlite=3.33.0=h4cf870e_0
-  - tk=8.6.10=hed695b0_0
-  - trf=4.09.1=h516909a_0
-  - wheel=0.35.1=pyh9f0ad1d_0
-  - xorg-kbproto=1.0.7=h14c3975_1002
-  - xorg-libice=1.0.10=h516909a_0
-  - xorg-libsm=1.2.3=h84519dc_1000
-  - xorg-libx11=1.6.12=h516909a_0
-  - xorg-libxau=1.0.9=h14c3975_0
-  - xorg-libxdmcp=1.1.3=h516909a_0
-  - xorg-libxext=1.3.4=h516909a_0
-  - xorg-libxrender=0.9.10=h516909a_1002
-  - xorg-renderproto=0.11.1=h14c3975_1002
-  - xorg-xextproto=7.3.0=h14c3975_1002
-  - xorg-xproto=7.0.31=h14c3975_1007
-  - xz=5.2.5=h516909a_1
-  - zlib=1.2.11=h516909a_1009
-prefix: /home/lecook/.conda/envs/wga
-
diff --git a/cross_species_comparison/scripts/axtChain_commands.sh b/cross_species_comparison/scripts/axtChain_commands.sh
deleted file mode 100644
index 27f45529816fd755a404c35502b8d9410e88bc9d..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/axtChain_commands.sh
+++ /dev/null
@@ -1,718 +0,0 @@
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00001_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00001_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00002_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00002_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00003_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00003_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00004_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00004_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00005_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00005_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00006_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00006_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00007_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00007_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00008_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00008_pilon_pilon_mm10.smiCra1.chain
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-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00687_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00687_pilon_pilon_mm10.smiCra1.chain
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-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00697_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00697_pilon_pilon_mm10.smiCra1.chain
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-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00702_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00702_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00703_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00703_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00704_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00704_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00705_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00705_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00706_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00706_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00707_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00707_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00708_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00708_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00709_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00709_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00710_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00710_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00711_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00711_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00712_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00712_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00713_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00713_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00714_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00714_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00715_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00715_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00716_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00716_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00717_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00717_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00718_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00718_pilon_pilon_mm10.smiCra1.chain
-axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/scaffold00719_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00719_pilon_pilon_mm10.smiCra1.chain
diff --git a/cross_species_comparison/scripts/chainSort_commands.sh b/cross_species_comparison/scripts/chainSort_commands.sh
deleted file mode 100644
index 5964d5f091b8329ccc8893545360e6c48c803dcf..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/chainSort_commands.sh
+++ /dev/null
@@ -1,713 +0,0 @@
-chainSort scaffold00001_pilon_pilon_mm10.smiCra1.chain > scaffold00001_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00002_pilon_pilon_mm10.smiCra1.chain > scaffold00002_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00003_pilon_pilon_mm10.smiCra1.chain > scaffold00003_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00004_pilon_pilon_mm10.smiCra1.chain > scaffold00004_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00005_pilon_pilon_mm10.smiCra1.chain > scaffold00005_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00006_pilon_pilon_mm10.smiCra1.chain > scaffold00006_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00007_pilon_pilon_mm10.smiCra1.chain > scaffold00007_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00008_pilon_pilon_mm10.smiCra1.chain > scaffold00008_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00009_pilon_pilon_mm10.smiCra1.chain > scaffold00009_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00010_pilon_pilon_mm10.smiCra1.chain > scaffold00010_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00011_pilon_pilon_mm10.smiCra1.chain > scaffold00011_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00012_pilon_pilon_mm10.smiCra1.chain > scaffold00012_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00013_pilon_pilon_mm10.smiCra1.chain > scaffold00013_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00014_pilon_pilon_mm10.smiCra1.chain > scaffold00014_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00016_pilon_pilon_mm10.smiCra1.chain > scaffold00016_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00017_pilon_pilon_mm10.smiCra1.chain > scaffold00017_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00018_pilon_pilon_mm10.smiCra1.chain > scaffold00018_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00019_pilon_pilon_mm10.smiCra1.chain > scaffold00019_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00020_pilon_pilon_mm10.smiCra1.chain > scaffold00020_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00021_pilon_pilon_mm10.smiCra1.chain > scaffold00021_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00022_pilon_pilon_mm10.smiCra1.chain > scaffold00022_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00023_pilon_pilon_mm10.smiCra1.chain > scaffold00023_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00024_pilon_pilon_mm10.smiCra1.chain > scaffold00024_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00025_pilon_pilon_mm10.smiCra1.chain > scaffold00025_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00026_pilon_pilon_mm10.smiCra1.chain > scaffold00026_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00027_pilon_pilon_mm10.smiCra1.chain > scaffold00027_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00028_pilon_pilon_mm10.smiCra1.chain > scaffold00028_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00029_pilon_pilon_mm10.smiCra1.chain > scaffold00029_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00030_pilon_pilon_mm10.smiCra1.chain > scaffold00030_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00031_pilon_pilon_mm10.smiCra1.chain > scaffold00031_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00032_pilon_pilon_mm10.smiCra1.chain > scaffold00032_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00033_pilon_pilon_mm10.smiCra1.chain > scaffold00033_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00034_pilon_pilon_mm10.smiCra1.chain > scaffold00034_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00035_pilon_pilon_mm10.smiCra1.chain > scaffold00035_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00036_pilon_pilon_mm10.smiCra1.chain > scaffold00036_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00037_pilon_pilon_mm10.smiCra1.chain > scaffold00037_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00038_pilon_pilon_mm10.smiCra1.chain > scaffold00038_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00039_pilon_pilon_mm10.smiCra1.chain > scaffold00039_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00040_pilon_pilon_mm10.smiCra1.chain > scaffold00040_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00041_pilon_pilon_mm10.smiCra1.chain > scaffold00041_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00042_pilon_pilon_mm10.smiCra1.chain > scaffold00042_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00043_pilon_pilon_mm10.smiCra1.chain > scaffold00043_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00044_pilon_pilon_mm10.smiCra1.chain > scaffold00044_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00045_pilon_pilon_mm10.smiCra1.chain > scaffold00045_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00046_pilon_pilon_mm10.smiCra1.chain > scaffold00046_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00047_pilon_pilon_mm10.smiCra1.chain > scaffold00047_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00048_pilon_pilon_mm10.smiCra1.chain > scaffold00048_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00049_pilon_pilon_mm10.smiCra1.chain > scaffold00049_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00050_pilon_pilon_mm10.smiCra1.chain > scaffold00050_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00051_pilon_pilon_mm10.smiCra1.chain > scaffold00051_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00052_pilon_pilon_mm10.smiCra1.chain > scaffold00052_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00053_pilon_pilon_mm10.smiCra1.chain > scaffold00053_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00054_pilon_pilon_mm10.smiCra1.chain > scaffold00054_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00055_pilon_pilon_mm10.smiCra1.chain > scaffold00055_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00056_pilon_pilon_mm10.smiCra1.chain > scaffold00056_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00057_pilon_pilon_mm10.smiCra1.chain > scaffold00057_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00058_pilon_pilon_mm10.smiCra1.chain > scaffold00058_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00059_pilon_pilon_mm10.smiCra1.chain > scaffold00059_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00060_pilon_pilon_mm10.smiCra1.chain > scaffold00060_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00061_pilon_pilon_mm10.smiCra1.chain > scaffold00061_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00062_pilon_pilon_mm10.smiCra1.chain > scaffold00062_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00063_pilon_pilon_mm10.smiCra1.chain > scaffold00063_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00064_pilon_pilon_mm10.smiCra1.chain > scaffold00064_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00065_pilon_pilon_mm10.smiCra1.chain > scaffold00065_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00066_pilon_pilon_mm10.smiCra1.chain > scaffold00066_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00067_pilon_pilon_mm10.smiCra1.chain > scaffold00067_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00068_pilon_pilon_mm10.smiCra1.chain > scaffold00068_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00069_pilon_pilon_mm10.smiCra1.chain > scaffold00069_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00070_pilon_pilon_mm10.smiCra1.chain > scaffold00070_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00071_pilon_pilon_mm10.smiCra1.chain > scaffold00071_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00072_pilon_pilon_mm10.smiCra1.chain > scaffold00072_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00073_pilon_pilon_mm10.smiCra1.chain > scaffold00073_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00074_pilon_pilon_mm10.smiCra1.chain > scaffold00074_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00075_pilon_pilon_mm10.smiCra1.chain > scaffold00075_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00076_pilon_pilon_mm10.smiCra1.chain > scaffold00076_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00077_pilon_pilon_mm10.smiCra1.chain > scaffold00077_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00078_pilon_pilon_mm10.smiCra1.chain > scaffold00078_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00079_pilon_pilon_mm10.smiCra1.chain > scaffold00079_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00080_pilon_pilon_mm10.smiCra1.chain > scaffold00080_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00081_pilon_pilon_mm10.smiCra1.chain > scaffold00081_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00082_pilon_pilon_mm10.smiCra1.chain > scaffold00082_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00083_pilon_pilon_mm10.smiCra1.chain > scaffold00083_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00084_pilon_pilon_mm10.smiCra1.chain > scaffold00084_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00085_pilon_pilon_mm10.smiCra1.chain > scaffold00085_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00086_pilon_pilon_mm10.smiCra1.chain > scaffold00086_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00087_pilon_pilon_mm10.smiCra1.chain > scaffold00087_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00088_pilon_pilon_mm10.smiCra1.chain > scaffold00088_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00089_pilon_pilon_mm10.smiCra1.chain > scaffold00089_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00090_pilon_pilon_mm10.smiCra1.chain > scaffold00090_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00091_pilon_pilon_mm10.smiCra1.chain > scaffold00091_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00092_pilon_pilon_mm10.smiCra1.chain > scaffold00092_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00093_pilon_pilon_mm10.smiCra1.chain > scaffold00093_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00094_pilon_pilon_mm10.smiCra1.chain > scaffold00094_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00095_pilon_pilon_mm10.smiCra1.chain > scaffold00095_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00096_pilon_pilon_mm10.smiCra1.chain > scaffold00096_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00097_pilon_pilon_mm10.smiCra1.chain > scaffold00097_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00098_pilon_pilon_mm10.smiCra1.chain > scaffold00098_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00099_pilon_pilon_mm10.smiCra1.chain > scaffold00099_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00100_pilon_pilon_mm10.smiCra1.chain > scaffold00100_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00101_pilon_pilon_mm10.smiCra1.chain > scaffold00101_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00102_pilon_pilon_mm10.smiCra1.chain > scaffold00102_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00103_pilon_pilon_mm10.smiCra1.chain > scaffold00103_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00104_pilon_pilon_mm10.smiCra1.chain > scaffold00104_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00105_pilon_pilon_mm10.smiCra1.chain > scaffold00105_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00106_pilon_pilon_mm10.smiCra1.chain > scaffold00106_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00107_pilon_pilon_mm10.smiCra1.chain > scaffold00107_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00108_pilon_pilon_mm10.smiCra1.chain > scaffold00108_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00109_pilon_pilon_mm10.smiCra1.chain > scaffold00109_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00110_pilon_pilon_mm10.smiCra1.chain > scaffold00110_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00111_pilon_pilon_mm10.smiCra1.chain > scaffold00111_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00112_pilon_pilon_mm10.smiCra1.chain > scaffold00112_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00113_pilon_pilon_mm10.smiCra1.chain > scaffold00113_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00114_pilon_pilon_mm10.smiCra1.chain > scaffold00114_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00115_pilon_pilon_mm10.smiCra1.chain > scaffold00115_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00116_pilon_pilon_mm10.smiCra1.chain > scaffold00116_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00117_pilon_pilon_mm10.smiCra1.chain > scaffold00117_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00118_pilon_pilon_mm10.smiCra1.chain > scaffold00118_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00119_pilon_pilon_mm10.smiCra1.chain > scaffold00119_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00120_pilon_pilon_mm10.smiCra1.chain > scaffold00120_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00121_pilon_pilon_mm10.smiCra1.chain > scaffold00121_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00122_pilon_pilon_mm10.smiCra1.chain > scaffold00122_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00123_pilon_pilon_mm10.smiCra1.chain > scaffold00123_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00124_pilon_pilon_mm10.smiCra1.chain > scaffold00124_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00125_pilon_pilon_mm10.smiCra1.chain > scaffold00125_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00126_pilon_pilon_mm10.smiCra1.chain > scaffold00126_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00127_pilon_pilon_mm10.smiCra1.chain > scaffold00127_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00128_pilon_pilon_mm10.smiCra1.chain > scaffold00128_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00129_pilon_pilon_mm10.smiCra1.chain > scaffold00129_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00130_pilon_pilon_mm10.smiCra1.chain > scaffold00130_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00131_pilon_pilon_mm10.smiCra1.chain > scaffold00131_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00132_pilon_pilon_mm10.smiCra1.chain > scaffold00132_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00133_pilon_pilon_mm10.smiCra1.chain > scaffold00133_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00134_pilon_pilon_mm10.smiCra1.chain > scaffold00134_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00135_pilon_pilon_mm10.smiCra1.chain > scaffold00135_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00136_pilon_pilon_mm10.smiCra1.chain > scaffold00136_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00137_pilon_pilon_mm10.smiCra1.chain > scaffold00137_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00138_pilon_pilon_mm10.smiCra1.chain > scaffold00138_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00139_pilon_pilon_mm10.smiCra1.chain > scaffold00139_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00140_pilon_pilon_mm10.smiCra1.chain > scaffold00140_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00141_pilon_pilon_mm10.smiCra1.chain > scaffold00141_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00142_pilon_pilon_mm10.smiCra1.chain > scaffold00142_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00143_pilon_pilon_mm10.smiCra1.chain > scaffold00143_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00144_pilon_pilon_mm10.smiCra1.chain > scaffold00144_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00145_pilon_pilon_mm10.smiCra1.chain > scaffold00145_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00146_pilon_pilon_mm10.smiCra1.chain > scaffold00146_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00147_pilon_pilon_mm10.smiCra1.chain > scaffold00147_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00148_pilon_pilon_mm10.smiCra1.chain > scaffold00148_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00149_pilon_pilon_mm10.smiCra1.chain > scaffold00149_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00150_pilon_pilon_mm10.smiCra1.chain > scaffold00150_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00151_pilon_pilon_mm10.smiCra1.chain > scaffold00151_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00152_pilon_pilon_mm10.smiCra1.chain > scaffold00152_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00153_pilon_pilon_mm10.smiCra1.chain > scaffold00153_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00154_pilon_pilon_mm10.smiCra1.chain > scaffold00154_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00155_pilon_pilon_mm10.smiCra1.chain > scaffold00155_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00156_pilon_pilon_mm10.smiCra1.chain > scaffold00156_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00157_pilon_pilon_mm10.smiCra1.chain > scaffold00157_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00158_pilon_pilon_mm10.smiCra1.chain > scaffold00158_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00159_pilon_pilon_mm10.smiCra1.chain > scaffold00159_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00160_pilon_pilon_mm10.smiCra1.chain > scaffold00160_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00161_pilon_pilon_mm10.smiCra1.chain > scaffold00161_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00162_pilon_pilon_mm10.smiCra1.chain > scaffold00162_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00163_pilon_pilon_mm10.smiCra1.chain > scaffold00163_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00164_pilon_pilon_mm10.smiCra1.chain > scaffold00164_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00165_pilon_pilon_mm10.smiCra1.chain > scaffold00165_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00166_pilon_pilon_mm10.smiCra1.chain > scaffold00166_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00167_pilon_pilon_mm10.smiCra1.chain > scaffold00167_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00168_pilon_pilon_mm10.smiCra1.chain > scaffold00168_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00169_pilon_pilon_mm10.smiCra1.chain > scaffold00169_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00170_pilon_pilon_mm10.smiCra1.chain > scaffold00170_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00171_pilon_pilon_mm10.smiCra1.chain > scaffold00171_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00172_pilon_pilon_mm10.smiCra1.chain > scaffold00172_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00173_pilon_pilon_mm10.smiCra1.chain > scaffold00173_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00174_pilon_pilon_mm10.smiCra1.chain > scaffold00174_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00175_pilon_pilon_mm10.smiCra1.chain > scaffold00175_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00176_pilon_pilon_mm10.smiCra1.chain > scaffold00176_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00177_pilon_pilon_mm10.smiCra1.chain > scaffold00177_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00178_pilon_pilon_mm10.smiCra1.chain > scaffold00178_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00179_pilon_pilon_mm10.smiCra1.chain > scaffold00179_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00180_pilon_pilon_mm10.smiCra1.chain > scaffold00180_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00181_pilon_pilon_mm10.smiCra1.chain > scaffold00181_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00182_pilon_pilon_mm10.smiCra1.chain > scaffold00182_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00183_pilon_pilon_mm10.smiCra1.chain > scaffold00183_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00184_pilon_pilon_mm10.smiCra1.chain > scaffold00184_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00185_pilon_pilon_mm10.smiCra1.chain > scaffold00185_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00186_pilon_pilon_mm10.smiCra1.chain > scaffold00186_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00187_pilon_pilon_mm10.smiCra1.chain > scaffold00187_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00188_pilon_pilon_mm10.smiCra1.chain > scaffold00188_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00189_pilon_pilon_mm10.smiCra1.chain > scaffold00189_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00190_pilon_pilon_mm10.smiCra1.chain > scaffold00190_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00191_pilon_pilon_mm10.smiCra1.chain > scaffold00191_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00192_pilon_pilon_mm10.smiCra1.chain > scaffold00192_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00193_pilon_pilon_mm10.smiCra1.chain > scaffold00193_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00194_pilon_pilon_mm10.smiCra1.chain > scaffold00194_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00195_pilon_pilon_mm10.smiCra1.chain > scaffold00195_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00196_pilon_pilon_mm10.smiCra1.chain > scaffold00196_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00197_pilon_pilon_mm10.smiCra1.chain > scaffold00197_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00198_pilon_pilon_mm10.smiCra1.chain > scaffold00198_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00199_pilon_pilon_mm10.smiCra1.chain > scaffold00199_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00200_pilon_pilon_mm10.smiCra1.chain > scaffold00200_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00201_pilon_pilon_mm10.smiCra1.chain > scaffold00201_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00202_pilon_pilon_mm10.smiCra1.chain > scaffold00202_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00203_pilon_pilon_mm10.smiCra1.chain > scaffold00203_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00204_pilon_pilon_mm10.smiCra1.chain > scaffold00204_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00205_pilon_pilon_mm10.smiCra1.chain > scaffold00205_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00206_pilon_pilon_mm10.smiCra1.chain > scaffold00206_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00207_pilon_pilon_mm10.smiCra1.chain > scaffold00207_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00208_pilon_pilon_mm10.smiCra1.chain > scaffold00208_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00209_pilon_pilon_mm10.smiCra1.chain > scaffold00209_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00210_pilon_pilon_mm10.smiCra1.chain > scaffold00210_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00211_pilon_pilon_mm10.smiCra1.chain > scaffold00211_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00212_pilon_pilon_mm10.smiCra1.chain > scaffold00212_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00213_pilon_pilon_mm10.smiCra1.chain > scaffold00213_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00214_pilon_pilon_mm10.smiCra1.chain > scaffold00214_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00215_pilon_pilon_mm10.smiCra1.chain > scaffold00215_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00216_pilon_pilon_mm10.smiCra1.chain > scaffold00216_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00217_pilon_pilon_mm10.smiCra1.chain > scaffold00217_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00218_pilon_pilon_mm10.smiCra1.chain > scaffold00218_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00219_pilon_pilon_mm10.smiCra1.chain > scaffold00219_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00220_pilon_pilon_mm10.smiCra1.chain > scaffold00220_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00221_pilon_pilon_mm10.smiCra1.chain > scaffold00221_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00222_pilon_pilon_mm10.smiCra1.chain > scaffold00222_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00223_pilon_pilon_mm10.smiCra1.chain > scaffold00223_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00224_pilon_pilon_mm10.smiCra1.chain > scaffold00224_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00225_pilon_pilon_mm10.smiCra1.chain > scaffold00225_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00226_pilon_pilon_mm10.smiCra1.chain > scaffold00226_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00227_pilon_pilon_mm10.smiCra1.chain > scaffold00227_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00228_pilon_pilon_mm10.smiCra1.chain > scaffold00228_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00229_pilon_pilon_mm10.smiCra1.chain > scaffold00229_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00230_pilon_pilon_mm10.smiCra1.chain > scaffold00230_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00231_pilon_pilon_mm10.smiCra1.chain > scaffold00231_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00232_pilon_pilon_mm10.smiCra1.chain > scaffold00232_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00233_pilon_pilon_mm10.smiCra1.chain > scaffold00233_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00234_pilon_pilon_mm10.smiCra1.chain > scaffold00234_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00235_pilon_pilon_mm10.smiCra1.chain > scaffold00235_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00236_pilon_pilon_mm10.smiCra1.chain > scaffold00236_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00237_pilon_pilon_mm10.smiCra1.chain > scaffold00237_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00238_pilon_pilon_mm10.smiCra1.chain > scaffold00238_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00239_pilon_pilon_mm10.smiCra1.chain > scaffold00239_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00240_pilon_pilon_mm10.smiCra1.chain > scaffold00240_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00241_pilon_pilon_mm10.smiCra1.chain > scaffold00241_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00242_pilon_pilon_mm10.smiCra1.chain > scaffold00242_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00243_pilon_pilon_mm10.smiCra1.chain > scaffold00243_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00244_pilon_pilon_mm10.smiCra1.chain > scaffold00244_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00245_pilon_pilon_mm10.smiCra1.chain > scaffold00245_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00246_pilon_pilon_mm10.smiCra1.chain > scaffold00246_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00247_pilon_pilon_mm10.smiCra1.chain > scaffold00247_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00248_pilon_pilon_mm10.smiCra1.chain > scaffold00248_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00249_pilon_pilon_mm10.smiCra1.chain > scaffold00249_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00250_pilon_pilon_mm10.smiCra1.chain > scaffold00250_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00251_pilon_pilon_mm10.smiCra1.chain > scaffold00251_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00252_pilon_pilon_mm10.smiCra1.chain > scaffold00252_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00253_pilon_pilon_mm10.smiCra1.chain > scaffold00253_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00254_pilon_pilon_mm10.smiCra1.chain > scaffold00254_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00255_pilon_pilon_mm10.smiCra1.chain > scaffold00255_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00256_pilon_pilon_mm10.smiCra1.chain > scaffold00256_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00257_pilon_pilon_mm10.smiCra1.chain > scaffold00257_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00258_pilon_pilon_mm10.smiCra1.chain > scaffold00258_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00259_pilon_pilon_mm10.smiCra1.chain > scaffold00259_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00260_pilon_pilon_mm10.smiCra1.chain > scaffold00260_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00261_pilon_pilon_mm10.smiCra1.chain > scaffold00261_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00262_pilon_pilon_mm10.smiCra1.chain > scaffold00262_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00263_pilon_pilon_mm10.smiCra1.chain > scaffold00263_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00264_pilon_pilon_mm10.smiCra1.chain > scaffold00264_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00265_pilon_pilon_mm10.smiCra1.chain > scaffold00265_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00266_pilon_pilon_mm10.smiCra1.chain > scaffold00266_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00267_pilon_pilon_mm10.smiCra1.chain > scaffold00267_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00268_pilon_pilon_mm10.smiCra1.chain > scaffold00268_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00269_pilon_pilon_mm10.smiCra1.chain > scaffold00269_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00270_pilon_pilon_mm10.smiCra1.chain > scaffold00270_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00271_pilon_pilon_mm10.smiCra1.chain > scaffold00271_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00272_pilon_pilon_mm10.smiCra1.chain > scaffold00272_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00273_pilon_pilon_mm10.smiCra1.chain > scaffold00273_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00279_pilon_pilon_mm10.smiCra1.chain > scaffold00279_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00280_pilon_pilon_mm10.smiCra1.chain > scaffold00280_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00281_pilon_pilon_mm10.smiCra1.chain > scaffold00281_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00282_pilon_pilon_mm10.smiCra1.chain > scaffold00282_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00283_pilon_pilon_mm10.smiCra1.chain > scaffold00283_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00284_pilon_pilon_mm10.smiCra1.chain > scaffold00284_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00285_pilon_pilon_mm10.smiCra1.chain > scaffold00285_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00286_pilon_pilon_mm10.smiCra1.chain > scaffold00286_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00287_pilon_pilon_mm10.smiCra1.chain > scaffold00287_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00288_pilon_pilon_mm10.smiCra1.chain > scaffold00288_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00289_pilon_pilon_mm10.smiCra1.chain > scaffold00289_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00290_pilon_pilon_mm10.smiCra1.chain > scaffold00290_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00291_pilon_pilon_mm10.smiCra1.chain > scaffold00291_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00292_pilon_pilon_mm10.smiCra1.chain > scaffold00292_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00293_pilon_pilon_mm10.smiCra1.chain > scaffold00293_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00294_pilon_pilon_mm10.smiCra1.chain > scaffold00294_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00295_pilon_pilon_mm10.smiCra1.chain > scaffold00295_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00296_pilon_pilon_mm10.smiCra1.chain > scaffold00296_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00297_pilon_pilon_mm10.smiCra1.chain > scaffold00297_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00298_pilon_pilon_mm10.smiCra1.chain > scaffold00298_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00299_pilon_pilon_mm10.smiCra1.chain > scaffold00299_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00300_pilon_pilon_mm10.smiCra1.chain > scaffold00300_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00301_pilon_pilon_mm10.smiCra1.chain > scaffold00301_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00302_pilon_pilon_mm10.smiCra1.chain > scaffold00302_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00303_pilon_pilon_mm10.smiCra1.chain > scaffold00303_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00304_pilon_pilon_mm10.smiCra1.chain > scaffold00304_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00305_pilon_pilon_mm10.smiCra1.chain > scaffold00305_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00306_pilon_pilon_mm10.smiCra1.chain > scaffold00306_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00307_pilon_pilon_mm10.smiCra1.chain > scaffold00307_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00308_pilon_pilon_mm10.smiCra1.chain > scaffold00308_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00309_pilon_pilon_mm10.smiCra1.chain > scaffold00309_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00310_pilon_pilon_mm10.smiCra1.chain > scaffold00310_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00311_pilon_pilon_mm10.smiCra1.chain > scaffold00311_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00312_pilon_pilon_mm10.smiCra1.chain > scaffold00312_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00313_pilon_pilon_mm10.smiCra1.chain > scaffold00313_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00314_pilon_pilon_mm10.smiCra1.chain > scaffold00314_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00315_pilon_pilon_mm10.smiCra1.chain > scaffold00315_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00316_pilon_pilon_mm10.smiCra1.chain > scaffold00316_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00317_pilon_pilon_mm10.smiCra1.chain > scaffold00317_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00318_pilon_pilon_mm10.smiCra1.chain > scaffold00318_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00319_pilon_pilon_mm10.smiCra1.chain > scaffold00319_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00320_pilon_pilon_mm10.smiCra1.chain > scaffold00320_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00321_pilon_pilon_mm10.smiCra1.chain > scaffold00321_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00322_pilon_pilon_mm10.smiCra1.chain > scaffold00322_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00323_pilon_pilon_mm10.smiCra1.chain > scaffold00323_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00324_pilon_pilon_mm10.smiCra1.chain > scaffold00324_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00325_pilon_pilon_mm10.smiCra1.chain > scaffold00325_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00326_pilon_pilon_mm10.smiCra1.chain > scaffold00326_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00327_pilon_pilon_mm10.smiCra1.chain > scaffold00327_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00328_pilon_pilon_mm10.smiCra1.chain > scaffold00328_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00329_pilon_pilon_mm10.smiCra1.chain > scaffold00329_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00330_pilon_pilon_mm10.smiCra1.chain > scaffold00330_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00331_pilon_pilon_mm10.smiCra1.chain > scaffold00331_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00332_pilon_pilon_mm10.smiCra1.chain > scaffold00332_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00333_pilon_pilon_mm10.smiCra1.chain > scaffold00333_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00334_pilon_pilon_mm10.smiCra1.chain > scaffold00334_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00335_pilon_pilon_mm10.smiCra1.chain > scaffold00335_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00336_pilon_pilon_mm10.smiCra1.chain > scaffold00336_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00337_pilon_pilon_mm10.smiCra1.chain > scaffold00337_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00338_pilon_pilon_mm10.smiCra1.chain > scaffold00338_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00339_pilon_pilon_mm10.smiCra1.chain > scaffold00339_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00340_pilon_pilon_mm10.smiCra1.chain > scaffold00340_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00341_pilon_pilon_mm10.smiCra1.chain > scaffold00341_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00342_pilon_pilon_mm10.smiCra1.chain > scaffold00342_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00343_pilon_pilon_mm10.smiCra1.chain > scaffold00343_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00344_pilon_pilon_mm10.smiCra1.chain > scaffold00344_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00345_pilon_pilon_mm10.smiCra1.chain > scaffold00345_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00346_pilon_pilon_mm10.smiCra1.chain > scaffold00346_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00347_pilon_pilon_mm10.smiCra1.chain > scaffold00347_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00348_pilon_pilon_mm10.smiCra1.chain > scaffold00348_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00349_pilon_pilon_mm10.smiCra1.chain > scaffold00349_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00350_pilon_pilon_mm10.smiCra1.chain > scaffold00350_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00351_pilon_pilon_mm10.smiCra1.chain > scaffold00351_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00352_pilon_pilon_mm10.smiCra1.chain > scaffold00352_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00353_pilon_pilon_mm10.smiCra1.chain > scaffold00353_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00354_pilon_pilon_mm10.smiCra1.chain > scaffold00354_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00355_pilon_pilon_mm10.smiCra1.chain > scaffold00355_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00356_pilon_pilon_mm10.smiCra1.chain > scaffold00356_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00357_pilon_pilon_mm10.smiCra1.chain > scaffold00357_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00358_pilon_pilon_mm10.smiCra1.chain > scaffold00358_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00359_pilon_pilon_mm10.smiCra1.chain > scaffold00359_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00360_pilon_pilon_mm10.smiCra1.chain > scaffold00360_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00361_pilon_pilon_mm10.smiCra1.chain > scaffold00361_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00362_pilon_pilon_mm10.smiCra1.chain > scaffold00362_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00363_pilon_pilon_mm10.smiCra1.chain > scaffold00363_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00364_pilon_pilon_mm10.smiCra1.chain > scaffold00364_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00365_pilon_pilon_mm10.smiCra1.chain > scaffold00365_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00366_pilon_pilon_mm10.smiCra1.chain > scaffold00366_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00367_pilon_pilon_mm10.smiCra1.chain > scaffold00367_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00368_pilon_pilon_mm10.smiCra1.chain > scaffold00368_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00369_pilon_pilon_mm10.smiCra1.chain > scaffold00369_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00370_pilon_pilon_mm10.smiCra1.chain > scaffold00370_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00371_pilon_pilon_mm10.smiCra1.chain > scaffold00371_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00372_pilon_pilon_mm10.smiCra1.chain > scaffold00372_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00373_pilon_pilon_mm10.smiCra1.chain > scaffold00373_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00374_pilon_pilon_mm10.smiCra1.chain > scaffold00374_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00375_pilon_pilon_mm10.smiCra1.chain > scaffold00375_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00376_pilon_pilon_mm10.smiCra1.chain > scaffold00376_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00377_pilon_pilon_mm10.smiCra1.chain > scaffold00377_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00378_pilon_pilon_mm10.smiCra1.chain > scaffold00378_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00379_pilon_pilon_mm10.smiCra1.chain > scaffold00379_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00380_pilon_pilon_mm10.smiCra1.chain > scaffold00380_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00381_pilon_pilon_mm10.smiCra1.chain > scaffold00381_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00382_pilon_pilon_mm10.smiCra1.chain > scaffold00382_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00383_pilon_pilon_mm10.smiCra1.chain > scaffold00383_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00384_pilon_pilon_mm10.smiCra1.chain > scaffold00384_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00385_pilon_pilon_mm10.smiCra1.chain > scaffold00385_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00386_pilon_pilon_mm10.smiCra1.chain > scaffold00386_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00387_pilon_pilon_mm10.smiCra1.chain > scaffold00387_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00388_pilon_pilon_mm10.smiCra1.chain > scaffold00388_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00389_pilon_pilon_mm10.smiCra1.chain > scaffold00389_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00390_pilon_pilon_mm10.smiCra1.chain > scaffold00390_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00391_pilon_pilon_mm10.smiCra1.chain > scaffold00391_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00392_pilon_pilon_mm10.smiCra1.chain > scaffold00392_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00393_pilon_pilon_mm10.smiCra1.chain > scaffold00393_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00394_pilon_pilon_mm10.smiCra1.chain > scaffold00394_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00395_pilon_pilon_mm10.smiCra1.chain > scaffold00395_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00396_pilon_pilon_mm10.smiCra1.chain > scaffold00396_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00397_pilon_pilon_mm10.smiCra1.chain > scaffold00397_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00398_pilon_pilon_mm10.smiCra1.chain > scaffold00398_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00399_pilon_pilon_mm10.smiCra1.chain > scaffold00399_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00400_pilon_pilon_mm10.smiCra1.chain > scaffold00400_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00401_pilon_pilon_mm10.smiCra1.chain > scaffold00401_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00402_pilon_pilon_mm10.smiCra1.chain > scaffold00402_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00403_pilon_pilon_mm10.smiCra1.chain > scaffold00403_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00404_pilon_pilon_mm10.smiCra1.chain > scaffold00404_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00405_pilon_pilon_mm10.smiCra1.chain > scaffold00405_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00406_pilon_pilon_mm10.smiCra1.chain > scaffold00406_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00407_pilon_pilon_mm10.smiCra1.chain > scaffold00407_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00408_pilon_pilon_mm10.smiCra1.chain > scaffold00408_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00409_pilon_pilon_mm10.smiCra1.chain > scaffold00409_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00410_pilon_pilon_mm10.smiCra1.chain > scaffold00410_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00411_pilon_pilon_mm10.smiCra1.chain > scaffold00411_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00412_pilon_pilon_mm10.smiCra1.chain > scaffold00412_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00413_pilon_pilon_mm10.smiCra1.chain > scaffold00413_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00414_pilon_pilon_mm10.smiCra1.chain > scaffold00414_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00415_pilon_pilon_mm10.smiCra1.chain > scaffold00415_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00416_pilon_pilon_mm10.smiCra1.chain > scaffold00416_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00417_pilon_pilon_mm10.smiCra1.chain > scaffold00417_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00418_pilon_pilon_mm10.smiCra1.chain > scaffold00418_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00419_pilon_pilon_mm10.smiCra1.chain > scaffold00419_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00420_pilon_pilon_mm10.smiCra1.chain > scaffold00420_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00421_pilon_pilon_mm10.smiCra1.chain > scaffold00421_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00422_pilon_pilon_mm10.smiCra1.chain > scaffold00422_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00423_pilon_pilon_mm10.smiCra1.chain > scaffold00423_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00424_pilon_pilon_mm10.smiCra1.chain > scaffold00424_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00425_pilon_pilon_mm10.smiCra1.chain > scaffold00425_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00426_pilon_pilon_mm10.smiCra1.chain > scaffold00426_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00427_pilon_pilon_mm10.smiCra1.chain > scaffold00427_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00428_pilon_pilon_mm10.smiCra1.chain > scaffold00428_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00429_pilon_pilon_mm10.smiCra1.chain > scaffold00429_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00430_pilon_pilon_mm10.smiCra1.chain > scaffold00430_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00431_pilon_pilon_mm10.smiCra1.chain > scaffold00431_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00432_pilon_pilon_mm10.smiCra1.chain > scaffold00432_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00433_pilon_pilon_mm10.smiCra1.chain > scaffold00433_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00434_pilon_pilon_mm10.smiCra1.chain > scaffold00434_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00435_pilon_pilon_mm10.smiCra1.chain > scaffold00435_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00436_pilon_pilon_mm10.smiCra1.chain > scaffold00436_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00437_pilon_pilon_mm10.smiCra1.chain > scaffold00437_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00438_pilon_pilon_mm10.smiCra1.chain > scaffold00438_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00439_pilon_pilon_mm10.smiCra1.chain > scaffold00439_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00440_pilon_pilon_mm10.smiCra1.chain > scaffold00440_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00441_pilon_pilon_mm10.smiCra1.chain > scaffold00441_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00442_pilon_pilon_mm10.smiCra1.chain > scaffold00442_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00443_pilon_pilon_mm10.smiCra1.chain > scaffold00443_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00444_pilon_pilon_mm10.smiCra1.chain > scaffold00444_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00445_pilon_pilon_mm10.smiCra1.chain > scaffold00445_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00446_pilon_pilon_mm10.smiCra1.chain > scaffold00446_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00447_pilon_pilon_mm10.smiCra1.chain > scaffold00447_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00448_pilon_pilon_mm10.smiCra1.chain > scaffold00448_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00449_pilon_pilon_mm10.smiCra1.chain > scaffold00449_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00450_pilon_pilon_mm10.smiCra1.chain > scaffold00450_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00451_pilon_pilon_mm10.smiCra1.chain > scaffold00451_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00452_pilon_pilon_mm10.smiCra1.chain > scaffold00452_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00453_pilon_pilon_mm10.smiCra1.chain > scaffold00453_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00454_pilon_pilon_mm10.smiCra1.chain > scaffold00454_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00455_pilon_pilon_mm10.smiCra1.chain > scaffold00455_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00456_pilon_pilon_mm10.smiCra1.chain > scaffold00456_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00457_pilon_pilon_mm10.smiCra1.chain > scaffold00457_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00458_pilon_pilon_mm10.smiCra1.chain > scaffold00458_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00459_pilon_pilon_mm10.smiCra1.chain > scaffold00459_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00460_pilon_pilon_mm10.smiCra1.chain > scaffold00460_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00461_pilon_pilon_mm10.smiCra1.chain > scaffold00461_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00462_pilon_pilon_mm10.smiCra1.chain > scaffold00462_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00463_pilon_pilon_mm10.smiCra1.chain > scaffold00463_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00464_pilon_pilon_mm10.smiCra1.chain > scaffold00464_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00465_pilon_pilon_mm10.smiCra1.chain > scaffold00465_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00466_pilon_pilon_mm10.smiCra1.chain > scaffold00466_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00467_pilon_pilon_mm10.smiCra1.chain > scaffold00467_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00468_pilon_pilon_mm10.smiCra1.chain > scaffold00468_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00469_pilon_pilon_mm10.smiCra1.chain > scaffold00469_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00470_pilon_pilon_mm10.smiCra1.chain > scaffold00470_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00471_pilon_pilon_mm10.smiCra1.chain > scaffold00471_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00472_pilon_pilon_mm10.smiCra1.chain > scaffold00472_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00473_pilon_pilon_mm10.smiCra1.chain > scaffold00473_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00474_pilon_pilon_mm10.smiCra1.chain > scaffold00474_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00475_pilon_pilon_mm10.smiCra1.chain > scaffold00475_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00476_pilon_pilon_mm10.smiCra1.chain > scaffold00476_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00477_pilon_pilon_mm10.smiCra1.chain > scaffold00477_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00478_pilon_pilon_mm10.smiCra1.chain > scaffold00478_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00479_pilon_pilon_mm10.smiCra1.chain > scaffold00479_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00480_pilon_pilon_mm10.smiCra1.chain > scaffold00480_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00481_pilon_pilon_mm10.smiCra1.chain > scaffold00481_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00482_pilon_pilon_mm10.smiCra1.chain > scaffold00482_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00483_pilon_pilon_mm10.smiCra1.chain > scaffold00483_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00484_pilon_pilon_mm10.smiCra1.chain > scaffold00484_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00485_pilon_pilon_mm10.smiCra1.chain > scaffold00485_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00486_pilon_pilon_mm10.smiCra1.chain > scaffold00486_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00487_pilon_pilon_mm10.smiCra1.chain > scaffold00487_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00488_pilon_pilon_mm10.smiCra1.chain > scaffold00488_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00489_pilon_pilon_mm10.smiCra1.chain > scaffold00489_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00490_pilon_pilon_mm10.smiCra1.chain > scaffold00490_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00491_pilon_pilon_mm10.smiCra1.chain > scaffold00491_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00492_pilon_pilon_mm10.smiCra1.chain > scaffold00492_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00493_pilon_pilon_mm10.smiCra1.chain > scaffold00493_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00494_pilon_pilon_mm10.smiCra1.chain > scaffold00494_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00495_pilon_pilon_mm10.smiCra1.chain > scaffold00495_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00496_pilon_pilon_mm10.smiCra1.chain > scaffold00496_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00497_pilon_pilon_mm10.smiCra1.chain > scaffold00497_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00498_pilon_pilon_mm10.smiCra1.chain > scaffold00498_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00499_pilon_pilon_mm10.smiCra1.chain > scaffold00499_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00500_pilon_pilon_mm10.smiCra1.chain > scaffold00500_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00501_pilon_pilon_mm10.smiCra1.chain > scaffold00501_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00502_pilon_pilon_mm10.smiCra1.chain > scaffold00502_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00503_pilon_pilon_mm10.smiCra1.chain > scaffold00503_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00504_pilon_pilon_mm10.smiCra1.chain > scaffold00504_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00505_pilon_pilon_mm10.smiCra1.chain > scaffold00505_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00506_pilon_pilon_mm10.smiCra1.chain > scaffold00506_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00507_pilon_pilon_mm10.smiCra1.chain > scaffold00507_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00508_pilon_pilon_mm10.smiCra1.chain > scaffold00508_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00509_pilon_pilon_mm10.smiCra1.chain > scaffold00509_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00510_pilon_pilon_mm10.smiCra1.chain > scaffold00510_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00511_pilon_pilon_mm10.smiCra1.chain > scaffold00511_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00512_pilon_pilon_mm10.smiCra1.chain > scaffold00512_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00514_pilon_pilon_mm10.smiCra1.chain > scaffold00514_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00516_pilon_pilon_mm10.smiCra1.chain > scaffold00516_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00517_pilon_pilon_mm10.smiCra1.chain > scaffold00517_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00518_pilon_pilon_mm10.smiCra1.chain > scaffold00518_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00519_pilon_pilon_mm10.smiCra1.chain > scaffold00519_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00520_pilon_pilon_mm10.smiCra1.chain > scaffold00520_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00521_pilon_pilon_mm10.smiCra1.chain > scaffold00521_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00522_pilon_pilon_mm10.smiCra1.chain > scaffold00522_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00523_pilon_pilon_mm10.smiCra1.chain > scaffold00523_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00524_pilon_pilon_mm10.smiCra1.chain > scaffold00524_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00525_pilon_pilon_mm10.smiCra1.chain > scaffold00525_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00526_pilon_pilon_mm10.smiCra1.chain > scaffold00526_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00527_pilon_pilon_mm10.smiCra1.chain > scaffold00527_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00528_pilon_pilon_mm10.smiCra1.chain > scaffold00528_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00529_pilon_pilon_mm10.smiCra1.chain > scaffold00529_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00530_pilon_pilon_mm10.smiCra1.chain > scaffold00530_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00531_pilon_pilon_mm10.smiCra1.chain > scaffold00531_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00532_pilon_pilon_mm10.smiCra1.chain > scaffold00532_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00533_pilon_pilon_mm10.smiCra1.chain > scaffold00533_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00534_pilon_pilon_mm10.smiCra1.chain > scaffold00534_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00535_pilon_pilon_mm10.smiCra1.chain > scaffold00535_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00536_pilon_pilon_mm10.smiCra1.chain > scaffold00536_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00537_pilon_pilon_mm10.smiCra1.chain > scaffold00537_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00538_pilon_pilon_mm10.smiCra1.chain > scaffold00538_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00539_pilon_pilon_mm10.smiCra1.chain > scaffold00539_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00540_pilon_pilon_mm10.smiCra1.chain > scaffold00540_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00541_pilon_pilon_mm10.smiCra1.chain > scaffold00541_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00542_pilon_pilon_mm10.smiCra1.chain > scaffold00542_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00543_pilon_pilon_mm10.smiCra1.chain > scaffold00543_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00544_pilon_pilon_mm10.smiCra1.chain > scaffold00544_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00545_pilon_pilon_mm10.smiCra1.chain > scaffold00545_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00546_pilon_pilon_mm10.smiCra1.chain > scaffold00546_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00547_pilon_pilon_mm10.smiCra1.chain > scaffold00547_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00548_pilon_pilon_mm10.smiCra1.chain > scaffold00548_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00549_pilon_pilon_mm10.smiCra1.chain > scaffold00549_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00550_pilon_pilon_mm10.smiCra1.chain > scaffold00550_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00551_pilon_pilon_mm10.smiCra1.chain > scaffold00551_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00553_pilon_pilon_mm10.smiCra1.chain > scaffold00553_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00554_pilon_pilon_mm10.smiCra1.chain > scaffold00554_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00555_pilon_pilon_mm10.smiCra1.chain > scaffold00555_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00556_pilon_pilon_mm10.smiCra1.chain > scaffold00556_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00557_pilon_pilon_mm10.smiCra1.chain > scaffold00557_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00558_pilon_pilon_mm10.smiCra1.chain > scaffold00558_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00559_pilon_pilon_mm10.smiCra1.chain > scaffold00559_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00560_pilon_pilon_mm10.smiCra1.chain > scaffold00560_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00561_pilon_pilon_mm10.smiCra1.chain > scaffold00561_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00562_pilon_pilon_mm10.smiCra1.chain > scaffold00562_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00563_pilon_pilon_mm10.smiCra1.chain > scaffold00563_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00564_pilon_pilon_mm10.smiCra1.chain > scaffold00564_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00565_pilon_pilon_mm10.smiCra1.chain > scaffold00565_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00566_pilon_pilon_mm10.smiCra1.chain > scaffold00566_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00567_pilon_pilon_mm10.smiCra1.chain > scaffold00567_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00568_pilon_pilon_mm10.smiCra1.chain > scaffold00568_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00569_pilon_pilon_mm10.smiCra1.chain > scaffold00569_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00570_pilon_pilon_mm10.smiCra1.chain > scaffold00570_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00571_pilon_pilon_mm10.smiCra1.chain > scaffold00571_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00572_pilon_pilon_mm10.smiCra1.chain > scaffold00572_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00573_pilon_pilon_mm10.smiCra1.chain > scaffold00573_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00574_pilon_pilon_mm10.smiCra1.chain > scaffold00574_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00575_pilon_pilon_mm10.smiCra1.chain > scaffold00575_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00576_pilon_pilon_mm10.smiCra1.chain > scaffold00576_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00577_pilon_pilon_mm10.smiCra1.chain > scaffold00577_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00578_pilon_pilon_mm10.smiCra1.chain > scaffold00578_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00579_pilon_pilon_mm10.smiCra1.chain > scaffold00579_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00580_pilon_pilon_mm10.smiCra1.chain > scaffold00580_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00581_pilon_pilon_mm10.smiCra1.chain > scaffold00581_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00582_pilon_pilon_mm10.smiCra1.chain > scaffold00582_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00583_pilon_pilon_mm10.smiCra1.chain > scaffold00583_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00584_pilon_pilon_mm10.smiCra1.chain > scaffold00584_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00585_pilon_pilon_mm10.smiCra1.chain > scaffold00585_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00586_pilon_pilon_mm10.smiCra1.chain > scaffold00586_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00587_pilon_pilon_mm10.smiCra1.chain > scaffold00587_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00588_pilon_pilon_mm10.smiCra1.chain > scaffold00588_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00589_pilon_pilon_mm10.smiCra1.chain > scaffold00589_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00590_pilon_pilon_mm10.smiCra1.chain > scaffold00590_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00591_pilon_pilon_mm10.smiCra1.chain > scaffold00591_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00592_pilon_pilon_mm10.smiCra1.chain > scaffold00592_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00593_pilon_pilon_mm10.smiCra1.chain > scaffold00593_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00594_pilon_pilon_mm10.smiCra1.chain > scaffold00594_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00595_pilon_pilon_mm10.smiCra1.chain > scaffold00595_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00596_pilon_pilon_mm10.smiCra1.chain > scaffold00596_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00597_pilon_pilon_mm10.smiCra1.chain > scaffold00597_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00598_pilon_pilon_mm10.smiCra1.chain > scaffold00598_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00600_pilon_pilon_mm10.smiCra1.chain > scaffold00600_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00601_pilon_pilon_mm10.smiCra1.chain > scaffold00601_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00602_pilon_pilon_mm10.smiCra1.chain > scaffold00602_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00603_pilon_pilon_mm10.smiCra1.chain > scaffold00603_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00604_pilon_pilon_mm10.smiCra1.chain > scaffold00604_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00605_pilon_pilon_mm10.smiCra1.chain > scaffold00605_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00606_pilon_pilon_mm10.smiCra1.chain > scaffold00606_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00607_pilon_pilon_mm10.smiCra1.chain > scaffold00607_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00608_pilon_pilon_mm10.smiCra1.chain > scaffold00608_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00609_pilon_pilon_mm10.smiCra1.chain > scaffold00609_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00610_pilon_pilon_mm10.smiCra1.chain > scaffold00610_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00612_pilon_pilon_mm10.smiCra1.chain > scaffold00612_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00613_pilon_pilon_mm10.smiCra1.chain > scaffold00613_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00617_pilon_pilon_mm10.smiCra1.chain > scaffold00617_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00618_pilon_pilon_mm10.smiCra1.chain > scaffold00618_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00619_pilon_pilon_mm10.smiCra1.chain > scaffold00619_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00620_pilon_pilon_mm10.smiCra1.chain > scaffold00620_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00621_pilon_pilon_mm10.smiCra1.chain > scaffold00621_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00622_pilon_pilon_mm10.smiCra1.chain > scaffold00622_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00623_pilon_pilon_mm10.smiCra1.chain > scaffold00623_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00624_pilon_pilon_mm10.smiCra1.chain > scaffold00624_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00625_pilon_pilon_mm10.smiCra1.chain > scaffold00625_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00626_pilon_pilon_mm10.smiCra1.chain > scaffold00626_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00627_pilon_pilon_mm10.smiCra1.chain > scaffold00627_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00628_pilon_pilon_mm10.smiCra1.chain > scaffold00628_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00633_pilon_pilon_mm10.smiCra1.chain > scaffold00633_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00637_pilon_pilon_mm10.smiCra1.chain > scaffold00637_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00639_pilon_pilon_mm10.smiCra1.chain > scaffold00639_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00642_pilon_pilon_mm10.smiCra1.chain > scaffold00642_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00643_pilon_pilon_mm10.smiCra1.chain > scaffold00643_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00644_pilon_pilon_mm10.smiCra1.chain > scaffold00644_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00645_pilon_pilon_mm10.smiCra1.chain > scaffold00645_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00646_pilon_pilon_mm10.smiCra1.chain > scaffold00646_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00647_pilon_pilon_mm10.smiCra1.chain > scaffold00647_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00648_pilon_pilon_mm10.smiCra1.chain > scaffold00648_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00649_pilon_pilon_mm10.smiCra1.chain > scaffold00649_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00650_pilon_pilon_mm10.smiCra1.chain > scaffold00650_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00651_pilon_pilon_mm10.smiCra1.chain > scaffold00651_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00652_pilon_pilon_mm10.smiCra1.chain > scaffold00652_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00653_pilon_pilon_mm10.smiCra1.chain > scaffold00653_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00654_pilon_pilon_mm10.smiCra1.chain > scaffold00654_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00655_pilon_pilon_mm10.smiCra1.chain > scaffold00655_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00656_pilon_pilon_mm10.smiCra1.chain > scaffold00656_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00657_pilon_pilon_mm10.smiCra1.chain > scaffold00657_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00658_pilon_pilon_mm10.smiCra1.chain > scaffold00658_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00659_pilon_pilon_mm10.smiCra1.chain > scaffold00659_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00660_pilon_pilon_mm10.smiCra1.chain > scaffold00660_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00661_pilon_pilon_mm10.smiCra1.chain > scaffold00661_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00662_pilon_pilon_mm10.smiCra1.chain > scaffold00662_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00663_pilon_pilon_mm10.smiCra1.chain > scaffold00663_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00664_pilon_pilon_mm10.smiCra1.chain > scaffold00664_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00665_pilon_pilon_mm10.smiCra1.chain > scaffold00665_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00666_pilon_pilon_mm10.smiCra1.chain > scaffold00666_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00667_pilon_pilon_mm10.smiCra1.chain > scaffold00667_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00668_pilon_pilon_mm10.smiCra1.chain > scaffold00668_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00669_pilon_pilon_mm10.smiCra1.chain > scaffold00669_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00670_pilon_pilon_mm10.smiCra1.chain > scaffold00670_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00671_pilon_pilon_mm10.smiCra1.chain > scaffold00671_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00672_pilon_pilon_mm10.smiCra1.chain > scaffold00672_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00673_pilon_pilon_mm10.smiCra1.chain > scaffold00673_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00674_pilon_pilon_mm10.smiCra1.chain > scaffold00674_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00675_pilon_pilon_mm10.smiCra1.chain > scaffold00675_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00676_pilon_pilon_mm10.smiCra1.chain > scaffold00676_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00677_pilon_pilon_mm10.smiCra1.chain > scaffold00677_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00678_pilon_pilon_mm10.smiCra1.chain > scaffold00678_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00679_pilon_pilon_mm10.smiCra1.chain > scaffold00679_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00682_pilon_pilon_mm10.smiCra1.chain > scaffold00682_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00683_pilon_pilon_mm10.smiCra1.chain > scaffold00683_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00684_pilon_pilon_mm10.smiCra1.chain > scaffold00684_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00685_pilon_pilon_mm10.smiCra1.chain > scaffold00685_pilon_pilon_mm10.smiCra1.sorted.chain
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-chainSort scaffold00688_pilon_pilon_mm10.smiCra1.chain > scaffold00688_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00689_pilon_pilon_mm10.smiCra1.chain > scaffold00689_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00690_pilon_pilon_mm10.smiCra1.chain > scaffold00690_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00691_pilon_pilon_mm10.smiCra1.chain > scaffold00691_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00692_pilon_pilon_mm10.smiCra1.chain > scaffold00692_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00693_pilon_pilon_mm10.smiCra1.chain > scaffold00693_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00694_pilon_pilon_mm10.smiCra1.chain > scaffold00694_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00695_pilon_pilon_mm10.smiCra1.chain > scaffold00695_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00696_pilon_pilon_mm10.smiCra1.chain > scaffold00696_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00697_pilon_pilon_mm10.smiCra1.chain > scaffold00697_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00698_pilon_pilon_mm10.smiCra1.chain > scaffold00698_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00699_pilon_pilon_mm10.smiCra1.chain > scaffold00699_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00700_pilon_pilon_mm10.smiCra1.chain > scaffold00700_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00701_pilon_pilon_mm10.smiCra1.chain > scaffold00701_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00702_pilon_pilon_mm10.smiCra1.chain > scaffold00702_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00703_pilon_pilon_mm10.smiCra1.chain > scaffold00703_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00704_pilon_pilon_mm10.smiCra1.chain > scaffold00704_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00705_pilon_pilon_mm10.smiCra1.chain > scaffold00705_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00706_pilon_pilon_mm10.smiCra1.chain > scaffold00706_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00707_pilon_pilon_mm10.smiCra1.chain > scaffold00707_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00708_pilon_pilon_mm10.smiCra1.chain > scaffold00708_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00709_pilon_pilon_mm10.smiCra1.chain > scaffold00709_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00710_pilon_pilon_mm10.smiCra1.chain > scaffold00710_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00711_pilon_pilon_mm10.smiCra1.chain > scaffold00711_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00712_pilon_pilon_mm10.smiCra1.chain > scaffold00712_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00713_pilon_pilon_mm10.smiCra1.chain > scaffold00713_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00714_pilon_pilon_mm10.smiCra1.chain > scaffold00714_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00715_pilon_pilon_mm10.smiCra1.chain > scaffold00715_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00716_pilon_pilon_mm10.smiCra1.chain > scaffold00716_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00717_pilon_pilon_mm10.smiCra1.chain > scaffold00717_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00718_pilon_pilon_mm10.smiCra1.chain > scaffold00718_pilon_pilon_mm10.smiCra1.sorted.chain
-chainSort scaffold00719_pilon_pilon_mm10.smiCra1.chain > scaffold00719_pilon_pilon_mm10.smiCra1.sorted.chain
diff --git a/cross_species_comparison/scripts/changeFilenameLoop.sh b/cross_species_comparison/scripts/changeFilenameLoop.sh
deleted file mode 100644
index e41606ca05bc24ce253649acc8f44a968716b0b7..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/changeFilenameLoop.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-##!/usr/bin/env bash
-
-## Change file names in a loop
-
-TRA=($(for file in *.fa.masked; do echo $file |cut -d "." -f 1;done))
-
-echo ${TRA[@]}
-
-for tr in ${TRA[@]};
-
-do
-
-mv ${tr}.fa.masked ${tr}.fa
-
-done
\ No newline at end of file
diff --git a/cross_species_comparison/scripts/lastz.sh b/cross_species_comparison/scripts/lastz.sh
deleted file mode 100644
index 901712968368531ec8a03d520e3b9caf0ef3597e..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/lastz.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-## This script loops through all scaffolds in the dunnart genome and
-## generates a separate command for each scaffold
-## These commands are then used in a slurm array script to run jobs in parallel
-
-TRA=($(for file in *.chain; do echo $file |cut -d "." -f 1-2;done))
-
-echo ${TRA[@]}
-
-for tr in ${TRA[@]};
-
-do
-
-# generate lastz commands
-#echo 'lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/'${tr}.fa 'H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/'${tr}_mm10.smiCra1.maf
-
-# generate convert maf to axt format commands
-#echo 'maf-convert axt '${tr}.maf' > '${tr}.axt
-
-## build co-linear alignment chains
-#echo 'axtChain -linearGap=loose /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/axt/'${tr}'.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit '${tr}'.chain'
-
-echo 'chainSort '${tr}'.chain > '${tr}'.sorted.chain'
-
-done
diff --git a/cross_species_comparison/scripts/lastz_commands.sh b/cross_species_comparison/scripts/lastz_commands.sh
deleted file mode 100644
index a382934efc56e6efcb2175eff49094ef71e5df8f..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/lastz_commands.sh
+++ /dev/null
@@ -1,718 +0,0 @@
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00001_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00001_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00003_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00003_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00004_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00004_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00005_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00005_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00006_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00006_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00007_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00007_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00008_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00008_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00009_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00009_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00010_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00010_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00011_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00011_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00012_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00012_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00013_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00013_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00014_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00014_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00015_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00015_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00016_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00016_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00017_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00017_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00018_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00018_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00019_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00019_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00020_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00020_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00021_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00021_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00022_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00022_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00023_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00023_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00024_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00024_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00025_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00025_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00026_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00026_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00027_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00027_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00028_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00028_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00029_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00029_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00030_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00030_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00031_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00031_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00032_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00032_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00033_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00033_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00034_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00034_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00035_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00035_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00036_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00036_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00037_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00037_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00038_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00038_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00039_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00039_pilon_pilon_mm10.smiCra1.maf
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-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00714_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00714_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00715_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00715_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00716_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00716_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00717_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00717_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00718_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00718_pilon_pilon_mm10.smiCra1.maf
-lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00719_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/scaffold00719_pilon_pilon_mm10.smiCra1.maf
diff --git a/cross_species_comparison/scripts/maf-convert_commands.sh b/cross_species_comparison/scripts/maf-convert_commands.sh
deleted file mode 100644
index cb3356419335fd627a761db237db760735a95517..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/maf-convert_commands.sh
+++ /dev/null
@@ -1,718 +0,0 @@
-maf-convert axt scaffold00001_pilon_pilon_mm10.smiCra1.maf > scaffold00001_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00002_pilon_pilon_mm10.smiCra1.maf > scaffold00002_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00003_pilon_pilon_mm10.smiCra1.maf > scaffold00003_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00004_pilon_pilon_mm10.smiCra1.maf > scaffold00004_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00005_pilon_pilon_mm10.smiCra1.maf > scaffold00005_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00006_pilon_pilon_mm10.smiCra1.maf > scaffold00006_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00007_pilon_pilon_mm10.smiCra1.maf > scaffold00007_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00008_pilon_pilon_mm10.smiCra1.maf > scaffold00008_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00009_pilon_pilon_mm10.smiCra1.maf > scaffold00009_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00010_pilon_pilon_mm10.smiCra1.maf > scaffold00010_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00011_pilon_pilon_mm10.smiCra1.maf > scaffold00011_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00012_pilon_pilon_mm10.smiCra1.maf > scaffold00012_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00013_pilon_pilon_mm10.smiCra1.maf > scaffold00013_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00014_pilon_pilon_mm10.smiCra1.maf > scaffold00014_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00016_pilon_pilon_mm10.smiCra1.maf > scaffold00016_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00017_pilon_pilon_mm10.smiCra1.maf > scaffold00017_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00018_pilon_pilon_mm10.smiCra1.maf > scaffold00018_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00019_pilon_pilon_mm10.smiCra1.maf > scaffold00019_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00020_pilon_pilon_mm10.smiCra1.maf > scaffold00020_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00021_pilon_pilon_mm10.smiCra1.maf > scaffold00021_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00022_pilon_pilon_mm10.smiCra1.maf > scaffold00022_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00023_pilon_pilon_mm10.smiCra1.maf > scaffold00023_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00024_pilon_pilon_mm10.smiCra1.maf > scaffold00024_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00025_pilon_pilon_mm10.smiCra1.maf > scaffold00025_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00026_pilon_pilon_mm10.smiCra1.maf > scaffold00026_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00027_pilon_pilon_mm10.smiCra1.maf > scaffold00027_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00028_pilon_pilon_mm10.smiCra1.maf > scaffold00028_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00029_pilon_pilon_mm10.smiCra1.maf > scaffold00029_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00030_pilon_pilon_mm10.smiCra1.maf > scaffold00030_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00031_pilon_pilon_mm10.smiCra1.maf > scaffold00031_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00032_pilon_pilon_mm10.smiCra1.maf > scaffold00032_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00033_pilon_pilon_mm10.smiCra1.maf > scaffold00033_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00034_pilon_pilon_mm10.smiCra1.maf > scaffold00034_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00035_pilon_pilon_mm10.smiCra1.maf > scaffold00035_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00036_pilon_pilon_mm10.smiCra1.maf > scaffold00036_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00037_pilon_pilon_mm10.smiCra1.maf > scaffold00037_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00038_pilon_pilon_mm10.smiCra1.maf > scaffold00038_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00039_pilon_pilon_mm10.smiCra1.maf > scaffold00039_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00040_pilon_pilon_mm10.smiCra1.maf > scaffold00040_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00041_pilon_pilon_mm10.smiCra1.maf > scaffold00041_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00042_pilon_pilon_mm10.smiCra1.maf > scaffold00042_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00043_pilon_pilon_mm10.smiCra1.maf > scaffold00043_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00044_pilon_pilon_mm10.smiCra1.maf > scaffold00044_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00045_pilon_pilon_mm10.smiCra1.maf > scaffold00045_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00046_pilon_pilon_mm10.smiCra1.maf > scaffold00046_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00047_pilon_pilon_mm10.smiCra1.maf > scaffold00047_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00048_pilon_pilon_mm10.smiCra1.maf > scaffold00048_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00049_pilon_pilon_mm10.smiCra1.maf > scaffold00049_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00050_pilon_pilon_mm10.smiCra1.maf > scaffold00050_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00051_pilon_pilon_mm10.smiCra1.maf > scaffold00051_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00052_pilon_pilon_mm10.smiCra1.maf > scaffold00052_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00053_pilon_pilon_mm10.smiCra1.maf > scaffold00053_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00054_pilon_pilon_mm10.smiCra1.maf > scaffold00054_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00055_pilon_pilon_mm10.smiCra1.maf > scaffold00055_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00056_pilon_pilon_mm10.smiCra1.maf > scaffold00056_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00057_pilon_pilon_mm10.smiCra1.maf > scaffold00057_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00058_pilon_pilon_mm10.smiCra1.maf > scaffold00058_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00059_pilon_pilon_mm10.smiCra1.maf > scaffold00059_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00060_pilon_pilon_mm10.smiCra1.maf > scaffold00060_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00061_pilon_pilon_mm10.smiCra1.maf > scaffold00061_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00062_pilon_pilon_mm10.smiCra1.maf > scaffold00062_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00063_pilon_pilon_mm10.smiCra1.maf > scaffold00063_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00064_pilon_pilon_mm10.smiCra1.maf > scaffold00064_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00065_pilon_pilon_mm10.smiCra1.maf > scaffold00065_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00066_pilon_pilon_mm10.smiCra1.maf > scaffold00066_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00067_pilon_pilon_mm10.smiCra1.maf > scaffold00067_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00068_pilon_pilon_mm10.smiCra1.maf > scaffold00068_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00069_pilon_pilon_mm10.smiCra1.maf > scaffold00069_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00070_pilon_pilon_mm10.smiCra1.maf > scaffold00070_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00071_pilon_pilon_mm10.smiCra1.maf > scaffold00071_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00072_pilon_pilon_mm10.smiCra1.maf > scaffold00072_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00073_pilon_pilon_mm10.smiCra1.maf > scaffold00073_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00074_pilon_pilon_mm10.smiCra1.maf > scaffold00074_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00075_pilon_pilon_mm10.smiCra1.maf > scaffold00075_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00076_pilon_pilon_mm10.smiCra1.maf > scaffold00076_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00077_pilon_pilon_mm10.smiCra1.maf > scaffold00077_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00078_pilon_pilon_mm10.smiCra1.maf > scaffold00078_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00079_pilon_pilon_mm10.smiCra1.maf > scaffold00079_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00080_pilon_pilon_mm10.smiCra1.maf > scaffold00080_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00081_pilon_pilon_mm10.smiCra1.maf > scaffold00081_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00082_pilon_pilon_mm10.smiCra1.maf > scaffold00082_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00083_pilon_pilon_mm10.smiCra1.maf > scaffold00083_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00084_pilon_pilon_mm10.smiCra1.maf > scaffold00084_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00085_pilon_pilon_mm10.smiCra1.maf > scaffold00085_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00086_pilon_pilon_mm10.smiCra1.maf > scaffold00086_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00087_pilon_pilon_mm10.smiCra1.maf > scaffold00087_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00088_pilon_pilon_mm10.smiCra1.maf > scaffold00088_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00089_pilon_pilon_mm10.smiCra1.maf > scaffold00089_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00090_pilon_pilon_mm10.smiCra1.maf > scaffold00090_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00091_pilon_pilon_mm10.smiCra1.maf > scaffold00091_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00092_pilon_pilon_mm10.smiCra1.maf > scaffold00092_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00093_pilon_pilon_mm10.smiCra1.maf > scaffold00093_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00094_pilon_pilon_mm10.smiCra1.maf > scaffold00094_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00095_pilon_pilon_mm10.smiCra1.maf > scaffold00095_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00096_pilon_pilon_mm10.smiCra1.maf > scaffold00096_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00097_pilon_pilon_mm10.smiCra1.maf > scaffold00097_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00098_pilon_pilon_mm10.smiCra1.maf > scaffold00098_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00099_pilon_pilon_mm10.smiCra1.maf > scaffold00099_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00100_pilon_pilon_mm10.smiCra1.maf > scaffold00100_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00101_pilon_pilon_mm10.smiCra1.maf > scaffold00101_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00102_pilon_pilon_mm10.smiCra1.maf > scaffold00102_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00103_pilon_pilon_mm10.smiCra1.maf > scaffold00103_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00104_pilon_pilon_mm10.smiCra1.maf > scaffold00104_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00105_pilon_pilon_mm10.smiCra1.maf > scaffold00105_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00106_pilon_pilon_mm10.smiCra1.maf > scaffold00106_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00107_pilon_pilon_mm10.smiCra1.maf > scaffold00107_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00108_pilon_pilon_mm10.smiCra1.maf > scaffold00108_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00109_pilon_pilon_mm10.smiCra1.maf > scaffold00109_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00110_pilon_pilon_mm10.smiCra1.maf > scaffold00110_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00111_pilon_pilon_mm10.smiCra1.maf > scaffold00111_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00112_pilon_pilon_mm10.smiCra1.maf > scaffold00112_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00113_pilon_pilon_mm10.smiCra1.maf > scaffold00113_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00114_pilon_pilon_mm10.smiCra1.maf > scaffold00114_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00115_pilon_pilon_mm10.smiCra1.maf > scaffold00115_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00116_pilon_pilon_mm10.smiCra1.maf > scaffold00116_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00117_pilon_pilon_mm10.smiCra1.maf > scaffold00117_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00118_pilon_pilon_mm10.smiCra1.maf > scaffold00118_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00119_pilon_pilon_mm10.smiCra1.maf > scaffold00119_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00120_pilon_pilon_mm10.smiCra1.maf > scaffold00120_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00121_pilon_pilon_mm10.smiCra1.maf > scaffold00121_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00122_pilon_pilon_mm10.smiCra1.maf > scaffold00122_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00123_pilon_pilon_mm10.smiCra1.maf > scaffold00123_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00124_pilon_pilon_mm10.smiCra1.maf > scaffold00124_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00125_pilon_pilon_mm10.smiCra1.maf > scaffold00125_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00126_pilon_pilon_mm10.smiCra1.maf > scaffold00126_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00127_pilon_pilon_mm10.smiCra1.maf > scaffold00127_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00128_pilon_pilon_mm10.smiCra1.maf > scaffold00128_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00129_pilon_pilon_mm10.smiCra1.maf > scaffold00129_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00130_pilon_pilon_mm10.smiCra1.maf > scaffold00130_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00131_pilon_pilon_mm10.smiCra1.maf > scaffold00131_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00132_pilon_pilon_mm10.smiCra1.maf > scaffold00132_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00133_pilon_pilon_mm10.smiCra1.maf > scaffold00133_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00134_pilon_pilon_mm10.smiCra1.maf > scaffold00134_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00135_pilon_pilon_mm10.smiCra1.maf > scaffold00135_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00136_pilon_pilon_mm10.smiCra1.maf > scaffold00136_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00137_pilon_pilon_mm10.smiCra1.maf > scaffold00137_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00138_pilon_pilon_mm10.smiCra1.maf > scaffold00138_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00139_pilon_pilon_mm10.smiCra1.maf > scaffold00139_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00140_pilon_pilon_mm10.smiCra1.maf > scaffold00140_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00141_pilon_pilon_mm10.smiCra1.maf > scaffold00141_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00142_pilon_pilon_mm10.smiCra1.maf > scaffold00142_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00143_pilon_pilon_mm10.smiCra1.maf > scaffold00143_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00144_pilon_pilon_mm10.smiCra1.maf > scaffold00144_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00145_pilon_pilon_mm10.smiCra1.maf > scaffold00145_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00146_pilon_pilon_mm10.smiCra1.maf > scaffold00146_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00147_pilon_pilon_mm10.smiCra1.maf > scaffold00147_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00148_pilon_pilon_mm10.smiCra1.maf > scaffold00148_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00149_pilon_pilon_mm10.smiCra1.maf > scaffold00149_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00150_pilon_pilon_mm10.smiCra1.maf > scaffold00150_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00151_pilon_pilon_mm10.smiCra1.maf > scaffold00151_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00152_pilon_pilon_mm10.smiCra1.maf > scaffold00152_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00153_pilon_pilon_mm10.smiCra1.maf > scaffold00153_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00154_pilon_pilon_mm10.smiCra1.maf > scaffold00154_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00155_pilon_pilon_mm10.smiCra1.maf > scaffold00155_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00156_pilon_pilon_mm10.smiCra1.maf > scaffold00156_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00157_pilon_pilon_mm10.smiCra1.maf > scaffold00157_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00158_pilon_pilon_mm10.smiCra1.maf > scaffold00158_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00159_pilon_pilon_mm10.smiCra1.maf > scaffold00159_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00160_pilon_pilon_mm10.smiCra1.maf > scaffold00160_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00161_pilon_pilon_mm10.smiCra1.maf > scaffold00161_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00162_pilon_pilon_mm10.smiCra1.maf > scaffold00162_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00163_pilon_pilon_mm10.smiCra1.maf > scaffold00163_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00164_pilon_pilon_mm10.smiCra1.maf > scaffold00164_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00165_pilon_pilon_mm10.smiCra1.maf > scaffold00165_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00166_pilon_pilon_mm10.smiCra1.maf > scaffold00166_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00167_pilon_pilon_mm10.smiCra1.maf > scaffold00167_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00168_pilon_pilon_mm10.smiCra1.maf > scaffold00168_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00169_pilon_pilon_mm10.smiCra1.maf > scaffold00169_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00170_pilon_pilon_mm10.smiCra1.maf > scaffold00170_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00171_pilon_pilon_mm10.smiCra1.maf > scaffold00171_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00172_pilon_pilon_mm10.smiCra1.maf > scaffold00172_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00173_pilon_pilon_mm10.smiCra1.maf > scaffold00173_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00174_pilon_pilon_mm10.smiCra1.maf > scaffold00174_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00175_pilon_pilon_mm10.smiCra1.maf > scaffold00175_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00176_pilon_pilon_mm10.smiCra1.maf > scaffold00176_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00177_pilon_pilon_mm10.smiCra1.maf > scaffold00177_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00178_pilon_pilon_mm10.smiCra1.maf > scaffold00178_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00179_pilon_pilon_mm10.smiCra1.maf > scaffold00179_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00180_pilon_pilon_mm10.smiCra1.maf > scaffold00180_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00181_pilon_pilon_mm10.smiCra1.maf > scaffold00181_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00182_pilon_pilon_mm10.smiCra1.maf > scaffold00182_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00183_pilon_pilon_mm10.smiCra1.maf > scaffold00183_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00184_pilon_pilon_mm10.smiCra1.maf > scaffold00184_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00185_pilon_pilon_mm10.smiCra1.maf > scaffold00185_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00186_pilon_pilon_mm10.smiCra1.maf > scaffold00186_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00187_pilon_pilon_mm10.smiCra1.maf > scaffold00187_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00188_pilon_pilon_mm10.smiCra1.maf > scaffold00188_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00189_pilon_pilon_mm10.smiCra1.maf > scaffold00189_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00190_pilon_pilon_mm10.smiCra1.maf > scaffold00190_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00191_pilon_pilon_mm10.smiCra1.maf > scaffold00191_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00192_pilon_pilon_mm10.smiCra1.maf > scaffold00192_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00193_pilon_pilon_mm10.smiCra1.maf > scaffold00193_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00194_pilon_pilon_mm10.smiCra1.maf > scaffold00194_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00195_pilon_pilon_mm10.smiCra1.maf > scaffold00195_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00196_pilon_pilon_mm10.smiCra1.maf > scaffold00196_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00197_pilon_pilon_mm10.smiCra1.maf > scaffold00197_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00198_pilon_pilon_mm10.smiCra1.maf > scaffold00198_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00199_pilon_pilon_mm10.smiCra1.maf > scaffold00199_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00200_pilon_pilon_mm10.smiCra1.maf > scaffold00200_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00201_pilon_pilon_mm10.smiCra1.maf > scaffold00201_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00202_pilon_pilon_mm10.smiCra1.maf > scaffold00202_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00203_pilon_pilon_mm10.smiCra1.maf > scaffold00203_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00204_pilon_pilon_mm10.smiCra1.maf > scaffold00204_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00205_pilon_pilon_mm10.smiCra1.maf > scaffold00205_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00206_pilon_pilon_mm10.smiCra1.maf > scaffold00206_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00207_pilon_pilon_mm10.smiCra1.maf > scaffold00207_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00208_pilon_pilon_mm10.smiCra1.maf > scaffold00208_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00209_pilon_pilon_mm10.smiCra1.maf > scaffold00209_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00210_pilon_pilon_mm10.smiCra1.maf > scaffold00210_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00211_pilon_pilon_mm10.smiCra1.maf > scaffold00211_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00212_pilon_pilon_mm10.smiCra1.maf > scaffold00212_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00213_pilon_pilon_mm10.smiCra1.maf > scaffold00213_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00214_pilon_pilon_mm10.smiCra1.maf > scaffold00214_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00215_pilon_pilon_mm10.smiCra1.maf > scaffold00215_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00216_pilon_pilon_mm10.smiCra1.maf > scaffold00216_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00217_pilon_pilon_mm10.smiCra1.maf > scaffold00217_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00218_pilon_pilon_mm10.smiCra1.maf > scaffold00218_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00219_pilon_pilon_mm10.smiCra1.maf > scaffold00219_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00220_pilon_pilon_mm10.smiCra1.maf > scaffold00220_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00221_pilon_pilon_mm10.smiCra1.maf > scaffold00221_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00222_pilon_pilon_mm10.smiCra1.maf > scaffold00222_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00223_pilon_pilon_mm10.smiCra1.maf > scaffold00223_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00224_pilon_pilon_mm10.smiCra1.maf > scaffold00224_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00225_pilon_pilon_mm10.smiCra1.maf > scaffold00225_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00226_pilon_pilon_mm10.smiCra1.maf > scaffold00226_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00227_pilon_pilon_mm10.smiCra1.maf > scaffold00227_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00228_pilon_pilon_mm10.smiCra1.maf > scaffold00228_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00229_pilon_pilon_mm10.smiCra1.maf > scaffold00229_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00230_pilon_pilon_mm10.smiCra1.maf > scaffold00230_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00231_pilon_pilon_mm10.smiCra1.maf > scaffold00231_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00232_pilon_pilon_mm10.smiCra1.maf > scaffold00232_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00233_pilon_pilon_mm10.smiCra1.maf > scaffold00233_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00234_pilon_pilon_mm10.smiCra1.maf > scaffold00234_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00235_pilon_pilon_mm10.smiCra1.maf > scaffold00235_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00236_pilon_pilon_mm10.smiCra1.maf > scaffold00236_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00237_pilon_pilon_mm10.smiCra1.maf > scaffold00237_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00238_pilon_pilon_mm10.smiCra1.maf > scaffold00238_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00239_pilon_pilon_mm10.smiCra1.maf > scaffold00239_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00240_pilon_pilon_mm10.smiCra1.maf > scaffold00240_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00241_pilon_pilon_mm10.smiCra1.maf > scaffold00241_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00242_pilon_pilon_mm10.smiCra1.maf > scaffold00242_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00243_pilon_pilon_mm10.smiCra1.maf > scaffold00243_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00244_pilon_pilon_mm10.smiCra1.maf > scaffold00244_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00245_pilon_pilon_mm10.smiCra1.maf > scaffold00245_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00246_pilon_pilon_mm10.smiCra1.maf > scaffold00246_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00247_pilon_pilon_mm10.smiCra1.maf > scaffold00247_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00248_pilon_pilon_mm10.smiCra1.maf > scaffold00248_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00249_pilon_pilon_mm10.smiCra1.maf > scaffold00249_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00250_pilon_pilon_mm10.smiCra1.maf > scaffold00250_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00251_pilon_pilon_mm10.smiCra1.maf > scaffold00251_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00252_pilon_pilon_mm10.smiCra1.maf > scaffold00252_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00253_pilon_pilon_mm10.smiCra1.maf > scaffold00253_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00254_pilon_pilon_mm10.smiCra1.maf > scaffold00254_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00255_pilon_pilon_mm10.smiCra1.maf > scaffold00255_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00256_pilon_pilon_mm10.smiCra1.maf > scaffold00256_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00257_pilon_pilon_mm10.smiCra1.maf > scaffold00257_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00258_pilon_pilon_mm10.smiCra1.maf > scaffold00258_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00259_pilon_pilon_mm10.smiCra1.maf > scaffold00259_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00260_pilon_pilon_mm10.smiCra1.maf > scaffold00260_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00261_pilon_pilon_mm10.smiCra1.maf > scaffold00261_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00262_pilon_pilon_mm10.smiCra1.maf > scaffold00262_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00263_pilon_pilon_mm10.smiCra1.maf > scaffold00263_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00264_pilon_pilon_mm10.smiCra1.maf > scaffold00264_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00265_pilon_pilon_mm10.smiCra1.maf > scaffold00265_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00266_pilon_pilon_mm10.smiCra1.maf > scaffold00266_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00267_pilon_pilon_mm10.smiCra1.maf > scaffold00267_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00268_pilon_pilon_mm10.smiCra1.maf > scaffold00268_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00269_pilon_pilon_mm10.smiCra1.maf > scaffold00269_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00270_pilon_pilon_mm10.smiCra1.maf > scaffold00270_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00271_pilon_pilon_mm10.smiCra1.maf > scaffold00271_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00272_pilon_pilon_mm10.smiCra1.maf > scaffold00272_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00273_pilon_pilon_mm10.smiCra1.maf > scaffold00273_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00274_pilon_pilon_mm10.smiCra1.maf > scaffold00274_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00275_pilon_pilon_mm10.smiCra1.maf > scaffold00275_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00276_pilon_pilon_mm10.smiCra1.maf > scaffold00276_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00277_pilon_pilon_mm10.smiCra1.maf > scaffold00277_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00278_pilon_pilon_mm10.smiCra1.maf > scaffold00278_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00279_pilon_pilon_mm10.smiCra1.maf > scaffold00279_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00280_pilon_pilon_mm10.smiCra1.maf > scaffold00280_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00281_pilon_pilon_mm10.smiCra1.maf > scaffold00281_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00282_pilon_pilon_mm10.smiCra1.maf > scaffold00282_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00283_pilon_pilon_mm10.smiCra1.maf > scaffold00283_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00284_pilon_pilon_mm10.smiCra1.maf > scaffold00284_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00285_pilon_pilon_mm10.smiCra1.maf > scaffold00285_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00286_pilon_pilon_mm10.smiCra1.maf > scaffold00286_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00287_pilon_pilon_mm10.smiCra1.maf > scaffold00287_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00288_pilon_pilon_mm10.smiCra1.maf > scaffold00288_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00289_pilon_pilon_mm10.smiCra1.maf > scaffold00289_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00290_pilon_pilon_mm10.smiCra1.maf > scaffold00290_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00291_pilon_pilon_mm10.smiCra1.maf > scaffold00291_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00292_pilon_pilon_mm10.smiCra1.maf > scaffold00292_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00293_pilon_pilon_mm10.smiCra1.maf > scaffold00293_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00294_pilon_pilon_mm10.smiCra1.maf > scaffold00294_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00295_pilon_pilon_mm10.smiCra1.maf > scaffold00295_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00296_pilon_pilon_mm10.smiCra1.maf > scaffold00296_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00297_pilon_pilon_mm10.smiCra1.maf > scaffold00297_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00298_pilon_pilon_mm10.smiCra1.maf > scaffold00298_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00299_pilon_pilon_mm10.smiCra1.maf > scaffold00299_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00300_pilon_pilon_mm10.smiCra1.maf > scaffold00300_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00301_pilon_pilon_mm10.smiCra1.maf > scaffold00301_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00302_pilon_pilon_mm10.smiCra1.maf > scaffold00302_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00303_pilon_pilon_mm10.smiCra1.maf > scaffold00303_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00304_pilon_pilon_mm10.smiCra1.maf > scaffold00304_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00305_pilon_pilon_mm10.smiCra1.maf > scaffold00305_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00306_pilon_pilon_mm10.smiCra1.maf > scaffold00306_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00307_pilon_pilon_mm10.smiCra1.maf > scaffold00307_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00308_pilon_pilon_mm10.smiCra1.maf > scaffold00308_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00309_pilon_pilon_mm10.smiCra1.maf > scaffold00309_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00310_pilon_pilon_mm10.smiCra1.maf > scaffold00310_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00311_pilon_pilon_mm10.smiCra1.maf > scaffold00311_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00312_pilon_pilon_mm10.smiCra1.maf > scaffold00312_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00313_pilon_pilon_mm10.smiCra1.maf > scaffold00313_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00314_pilon_pilon_mm10.smiCra1.maf > scaffold00314_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00315_pilon_pilon_mm10.smiCra1.maf > scaffold00315_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00316_pilon_pilon_mm10.smiCra1.maf > scaffold00316_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00317_pilon_pilon_mm10.smiCra1.maf > scaffold00317_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00318_pilon_pilon_mm10.smiCra1.maf > scaffold00318_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00319_pilon_pilon_mm10.smiCra1.maf > scaffold00319_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00320_pilon_pilon_mm10.smiCra1.maf > scaffold00320_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00321_pilon_pilon_mm10.smiCra1.maf > scaffold00321_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00322_pilon_pilon_mm10.smiCra1.maf > scaffold00322_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00323_pilon_pilon_mm10.smiCra1.maf > scaffold00323_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00324_pilon_pilon_mm10.smiCra1.maf > scaffold00324_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00325_pilon_pilon_mm10.smiCra1.maf > scaffold00325_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00326_pilon_pilon_mm10.smiCra1.maf > scaffold00326_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00327_pilon_pilon_mm10.smiCra1.maf > scaffold00327_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00328_pilon_pilon_mm10.smiCra1.maf > scaffold00328_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00329_pilon_pilon_mm10.smiCra1.maf > scaffold00329_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00330_pilon_pilon_mm10.smiCra1.maf > scaffold00330_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00331_pilon_pilon_mm10.smiCra1.maf > scaffold00331_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00332_pilon_pilon_mm10.smiCra1.maf > scaffold00332_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00333_pilon_pilon_mm10.smiCra1.maf > scaffold00333_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00334_pilon_pilon_mm10.smiCra1.maf > scaffold00334_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00335_pilon_pilon_mm10.smiCra1.maf > scaffold00335_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00336_pilon_pilon_mm10.smiCra1.maf > scaffold00336_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00337_pilon_pilon_mm10.smiCra1.maf > scaffold00337_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00338_pilon_pilon_mm10.smiCra1.maf > scaffold00338_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00339_pilon_pilon_mm10.smiCra1.maf > scaffold00339_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00340_pilon_pilon_mm10.smiCra1.maf > scaffold00340_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00341_pilon_pilon_mm10.smiCra1.maf > scaffold00341_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00342_pilon_pilon_mm10.smiCra1.maf > scaffold00342_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00343_pilon_pilon_mm10.smiCra1.maf > scaffold00343_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00344_pilon_pilon_mm10.smiCra1.maf > scaffold00344_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00345_pilon_pilon_mm10.smiCra1.maf > scaffold00345_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00346_pilon_pilon_mm10.smiCra1.maf > scaffold00346_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00347_pilon_pilon_mm10.smiCra1.maf > scaffold00347_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00348_pilon_pilon_mm10.smiCra1.maf > scaffold00348_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00349_pilon_pilon_mm10.smiCra1.maf > scaffold00349_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00350_pilon_pilon_mm10.smiCra1.maf > scaffold00350_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00351_pilon_pilon_mm10.smiCra1.maf > scaffold00351_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00352_pilon_pilon_mm10.smiCra1.maf > scaffold00352_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00353_pilon_pilon_mm10.smiCra1.maf > scaffold00353_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00354_pilon_pilon_mm10.smiCra1.maf > scaffold00354_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00355_pilon_pilon_mm10.smiCra1.maf > scaffold00355_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00356_pilon_pilon_mm10.smiCra1.maf > scaffold00356_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00357_pilon_pilon_mm10.smiCra1.maf > scaffold00357_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00358_pilon_pilon_mm10.smiCra1.maf > scaffold00358_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00359_pilon_pilon_mm10.smiCra1.maf > scaffold00359_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00360_pilon_pilon_mm10.smiCra1.maf > scaffold00360_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00361_pilon_pilon_mm10.smiCra1.maf > scaffold00361_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00362_pilon_pilon_mm10.smiCra1.maf > scaffold00362_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00363_pilon_pilon_mm10.smiCra1.maf > scaffold00363_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00364_pilon_pilon_mm10.smiCra1.maf > scaffold00364_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00365_pilon_pilon_mm10.smiCra1.maf > scaffold00365_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00366_pilon_pilon_mm10.smiCra1.maf > scaffold00366_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00367_pilon_pilon_mm10.smiCra1.maf > scaffold00367_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00368_pilon_pilon_mm10.smiCra1.maf > scaffold00368_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00369_pilon_pilon_mm10.smiCra1.maf > scaffold00369_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00370_pilon_pilon_mm10.smiCra1.maf > scaffold00370_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00371_pilon_pilon_mm10.smiCra1.maf > scaffold00371_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00372_pilon_pilon_mm10.smiCra1.maf > scaffold00372_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00373_pilon_pilon_mm10.smiCra1.maf > scaffold00373_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00374_pilon_pilon_mm10.smiCra1.maf > scaffold00374_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00375_pilon_pilon_mm10.smiCra1.maf > scaffold00375_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00376_pilon_pilon_mm10.smiCra1.maf > scaffold00376_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00377_pilon_pilon_mm10.smiCra1.maf > scaffold00377_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00378_pilon_pilon_mm10.smiCra1.maf > scaffold00378_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00379_pilon_pilon_mm10.smiCra1.maf > scaffold00379_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00380_pilon_pilon_mm10.smiCra1.maf > scaffold00380_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00381_pilon_pilon_mm10.smiCra1.maf > scaffold00381_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00382_pilon_pilon_mm10.smiCra1.maf > scaffold00382_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00383_pilon_pilon_mm10.smiCra1.maf > scaffold00383_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00384_pilon_pilon_mm10.smiCra1.maf > scaffold00384_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00385_pilon_pilon_mm10.smiCra1.maf > scaffold00385_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00386_pilon_pilon_mm10.smiCra1.maf > scaffold00386_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00387_pilon_pilon_mm10.smiCra1.maf > scaffold00387_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00388_pilon_pilon_mm10.smiCra1.maf > scaffold00388_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00389_pilon_pilon_mm10.smiCra1.maf > scaffold00389_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00390_pilon_pilon_mm10.smiCra1.maf > scaffold00390_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00391_pilon_pilon_mm10.smiCra1.maf > scaffold00391_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00392_pilon_pilon_mm10.smiCra1.maf > scaffold00392_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00393_pilon_pilon_mm10.smiCra1.maf > scaffold00393_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00394_pilon_pilon_mm10.smiCra1.maf > scaffold00394_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00395_pilon_pilon_mm10.smiCra1.maf > scaffold00395_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00396_pilon_pilon_mm10.smiCra1.maf > scaffold00396_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00397_pilon_pilon_mm10.smiCra1.maf > scaffold00397_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00398_pilon_pilon_mm10.smiCra1.maf > scaffold00398_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00399_pilon_pilon_mm10.smiCra1.maf > scaffold00399_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00400_pilon_pilon_mm10.smiCra1.maf > scaffold00400_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00401_pilon_pilon_mm10.smiCra1.maf > scaffold00401_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00402_pilon_pilon_mm10.smiCra1.maf > scaffold00402_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00403_pilon_pilon_mm10.smiCra1.maf > scaffold00403_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00404_pilon_pilon_mm10.smiCra1.maf > scaffold00404_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00405_pilon_pilon_mm10.smiCra1.maf > scaffold00405_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00406_pilon_pilon_mm10.smiCra1.maf > scaffold00406_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00407_pilon_pilon_mm10.smiCra1.maf > scaffold00407_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00408_pilon_pilon_mm10.smiCra1.maf > scaffold00408_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00409_pilon_pilon_mm10.smiCra1.maf > scaffold00409_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00410_pilon_pilon_mm10.smiCra1.maf > scaffold00410_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00411_pilon_pilon_mm10.smiCra1.maf > scaffold00411_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00412_pilon_pilon_mm10.smiCra1.maf > scaffold00412_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00413_pilon_pilon_mm10.smiCra1.maf > scaffold00413_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00414_pilon_pilon_mm10.smiCra1.maf > scaffold00414_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00415_pilon_pilon_mm10.smiCra1.maf > scaffold00415_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00416_pilon_pilon_mm10.smiCra1.maf > scaffold00416_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00417_pilon_pilon_mm10.smiCra1.maf > scaffold00417_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00418_pilon_pilon_mm10.smiCra1.maf > scaffold00418_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00419_pilon_pilon_mm10.smiCra1.maf > scaffold00419_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00420_pilon_pilon_mm10.smiCra1.maf > scaffold00420_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00421_pilon_pilon_mm10.smiCra1.maf > scaffold00421_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00422_pilon_pilon_mm10.smiCra1.maf > scaffold00422_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00423_pilon_pilon_mm10.smiCra1.maf > scaffold00423_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00424_pilon_pilon_mm10.smiCra1.maf > scaffold00424_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00425_pilon_pilon_mm10.smiCra1.maf > scaffold00425_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00426_pilon_pilon_mm10.smiCra1.maf > scaffold00426_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00427_pilon_pilon_mm10.smiCra1.maf > scaffold00427_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00428_pilon_pilon_mm10.smiCra1.maf > scaffold00428_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00429_pilon_pilon_mm10.smiCra1.maf > scaffold00429_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00430_pilon_pilon_mm10.smiCra1.maf > scaffold00430_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00431_pilon_pilon_mm10.smiCra1.maf > scaffold00431_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00432_pilon_pilon_mm10.smiCra1.maf > scaffold00432_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00433_pilon_pilon_mm10.smiCra1.maf > scaffold00433_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00434_pilon_pilon_mm10.smiCra1.maf > scaffold00434_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00435_pilon_pilon_mm10.smiCra1.maf > scaffold00435_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00436_pilon_pilon_mm10.smiCra1.maf > scaffold00436_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00437_pilon_pilon_mm10.smiCra1.maf > scaffold00437_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00438_pilon_pilon_mm10.smiCra1.maf > scaffold00438_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00439_pilon_pilon_mm10.smiCra1.maf > scaffold00439_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00440_pilon_pilon_mm10.smiCra1.maf > scaffold00440_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00441_pilon_pilon_mm10.smiCra1.maf > scaffold00441_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00442_pilon_pilon_mm10.smiCra1.maf > scaffold00442_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00443_pilon_pilon_mm10.smiCra1.maf > scaffold00443_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00444_pilon_pilon_mm10.smiCra1.maf > scaffold00444_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00445_pilon_pilon_mm10.smiCra1.maf > scaffold00445_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00446_pilon_pilon_mm10.smiCra1.maf > scaffold00446_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00447_pilon_pilon_mm10.smiCra1.maf > scaffold00447_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00448_pilon_pilon_mm10.smiCra1.maf > scaffold00448_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00449_pilon_pilon_mm10.smiCra1.maf > scaffold00449_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00450_pilon_pilon_mm10.smiCra1.maf > scaffold00450_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00451_pilon_pilon_mm10.smiCra1.maf > scaffold00451_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00452_pilon_pilon_mm10.smiCra1.maf > scaffold00452_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00453_pilon_pilon_mm10.smiCra1.maf > scaffold00453_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00454_pilon_pilon_mm10.smiCra1.maf > scaffold00454_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00455_pilon_pilon_mm10.smiCra1.maf > scaffold00455_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00456_pilon_pilon_mm10.smiCra1.maf > scaffold00456_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00457_pilon_pilon_mm10.smiCra1.maf > scaffold00457_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00458_pilon_pilon_mm10.smiCra1.maf > scaffold00458_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00459_pilon_pilon_mm10.smiCra1.maf > scaffold00459_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00460_pilon_pilon_mm10.smiCra1.maf > scaffold00460_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00461_pilon_pilon_mm10.smiCra1.maf > scaffold00461_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00462_pilon_pilon_mm10.smiCra1.maf > scaffold00462_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00463_pilon_pilon_mm10.smiCra1.maf > scaffold00463_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00464_pilon_pilon_mm10.smiCra1.maf > scaffold00464_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00465_pilon_pilon_mm10.smiCra1.maf > scaffold00465_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00466_pilon_pilon_mm10.smiCra1.maf > scaffold00466_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00467_pilon_pilon_mm10.smiCra1.maf > scaffold00467_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00468_pilon_pilon_mm10.smiCra1.maf > scaffold00468_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00469_pilon_pilon_mm10.smiCra1.maf > scaffold00469_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00470_pilon_pilon_mm10.smiCra1.maf > scaffold00470_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00471_pilon_pilon_mm10.smiCra1.maf > scaffold00471_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00472_pilon_pilon_mm10.smiCra1.maf > scaffold00472_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00473_pilon_pilon_mm10.smiCra1.maf > scaffold00473_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00474_pilon_pilon_mm10.smiCra1.maf > scaffold00474_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00475_pilon_pilon_mm10.smiCra1.maf > scaffold00475_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00476_pilon_pilon_mm10.smiCra1.maf > scaffold00476_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00477_pilon_pilon_mm10.smiCra1.maf > scaffold00477_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00478_pilon_pilon_mm10.smiCra1.maf > scaffold00478_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00479_pilon_pilon_mm10.smiCra1.maf > scaffold00479_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00480_pilon_pilon_mm10.smiCra1.maf > scaffold00480_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00481_pilon_pilon_mm10.smiCra1.maf > scaffold00481_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00482_pilon_pilon_mm10.smiCra1.maf > scaffold00482_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00483_pilon_pilon_mm10.smiCra1.maf > scaffold00483_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00484_pilon_pilon_mm10.smiCra1.maf > scaffold00484_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00485_pilon_pilon_mm10.smiCra1.maf > scaffold00485_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00486_pilon_pilon_mm10.smiCra1.maf > scaffold00486_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00487_pilon_pilon_mm10.smiCra1.maf > scaffold00487_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00488_pilon_pilon_mm10.smiCra1.maf > scaffold00488_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00489_pilon_pilon_mm10.smiCra1.maf > scaffold00489_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00490_pilon_pilon_mm10.smiCra1.maf > scaffold00490_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00491_pilon_pilon_mm10.smiCra1.maf > scaffold00491_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00492_pilon_pilon_mm10.smiCra1.maf > scaffold00492_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00493_pilon_pilon_mm10.smiCra1.maf > scaffold00493_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00494_pilon_pilon_mm10.smiCra1.maf > scaffold00494_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00495_pilon_pilon_mm10.smiCra1.maf > scaffold00495_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00496_pilon_pilon_mm10.smiCra1.maf > scaffold00496_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00497_pilon_pilon_mm10.smiCra1.maf > scaffold00497_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00498_pilon_pilon_mm10.smiCra1.maf > scaffold00498_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00499_pilon_pilon_mm10.smiCra1.maf > scaffold00499_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00500_pilon_pilon_mm10.smiCra1.maf > scaffold00500_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00501_pilon_pilon_mm10.smiCra1.maf > scaffold00501_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00502_pilon_pilon_mm10.smiCra1.maf > scaffold00502_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00503_pilon_pilon_mm10.smiCra1.maf > scaffold00503_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00504_pilon_pilon_mm10.smiCra1.maf > scaffold00504_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00505_pilon_pilon_mm10.smiCra1.maf > scaffold00505_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00506_pilon_pilon_mm10.smiCra1.maf > scaffold00506_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00507_pilon_pilon_mm10.smiCra1.maf > scaffold00507_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00508_pilon_pilon_mm10.smiCra1.maf > scaffold00508_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00509_pilon_pilon_mm10.smiCra1.maf > scaffold00509_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00510_pilon_pilon_mm10.smiCra1.maf > scaffold00510_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00511_pilon_pilon_mm10.smiCra1.maf > scaffold00511_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00512_pilon_pilon_mm10.smiCra1.maf > scaffold00512_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00513_pilon_pilon_mm10.smiCra1.maf > scaffold00513_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00514_pilon_pilon_mm10.smiCra1.maf > scaffold00514_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00515_pilon_pilon_mm10.smiCra1.maf > scaffold00515_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00516_pilon_pilon_mm10.smiCra1.maf > scaffold00516_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00517_pilon_pilon_mm10.smiCra1.maf > scaffold00517_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00518_pilon_pilon_mm10.smiCra1.maf > scaffold00518_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00519_pilon_pilon_mm10.smiCra1.maf > scaffold00519_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00520_pilon_pilon_mm10.smiCra1.maf > scaffold00520_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00521_pilon_pilon_mm10.smiCra1.maf > scaffold00521_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00522_pilon_pilon_mm10.smiCra1.maf > scaffold00522_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00523_pilon_pilon_mm10.smiCra1.maf > scaffold00523_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00524_pilon_pilon_mm10.smiCra1.maf > scaffold00524_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00525_pilon_pilon_mm10.smiCra1.maf > scaffold00525_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00526_pilon_pilon_mm10.smiCra1.maf > scaffold00526_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00527_pilon_pilon_mm10.smiCra1.maf > scaffold00527_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00528_pilon_pilon_mm10.smiCra1.maf > scaffold00528_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00529_pilon_pilon_mm10.smiCra1.maf > scaffold00529_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00530_pilon_pilon_mm10.smiCra1.maf > scaffold00530_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00531_pilon_pilon_mm10.smiCra1.maf > scaffold00531_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00532_pilon_pilon_mm10.smiCra1.maf > scaffold00532_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00533_pilon_pilon_mm10.smiCra1.maf > scaffold00533_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00534_pilon_pilon_mm10.smiCra1.maf > scaffold00534_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00535_pilon_pilon_mm10.smiCra1.maf > scaffold00535_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00536_pilon_pilon_mm10.smiCra1.maf > scaffold00536_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00537_pilon_pilon_mm10.smiCra1.maf > scaffold00537_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00538_pilon_pilon_mm10.smiCra1.maf > scaffold00538_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00539_pilon_pilon_mm10.smiCra1.maf > scaffold00539_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00540_pilon_pilon_mm10.smiCra1.maf > scaffold00540_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00541_pilon_pilon_mm10.smiCra1.maf > scaffold00541_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00542_pilon_pilon_mm10.smiCra1.maf > scaffold00542_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00543_pilon_pilon_mm10.smiCra1.maf > scaffold00543_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00544_pilon_pilon_mm10.smiCra1.maf > scaffold00544_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00545_pilon_pilon_mm10.smiCra1.maf > scaffold00545_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00546_pilon_pilon_mm10.smiCra1.maf > scaffold00546_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00547_pilon_pilon_mm10.smiCra1.maf > scaffold00547_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00548_pilon_pilon_mm10.smiCra1.maf > scaffold00548_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00549_pilon_pilon_mm10.smiCra1.maf > scaffold00549_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00550_pilon_pilon_mm10.smiCra1.maf > scaffold00550_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00551_pilon_pilon_mm10.smiCra1.maf > scaffold00551_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00552_pilon_pilon_mm10.smiCra1.maf > scaffold00552_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00553_pilon_pilon_mm10.smiCra1.maf > scaffold00553_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00554_pilon_pilon_mm10.smiCra1.maf > scaffold00554_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00555_pilon_pilon_mm10.smiCra1.maf > scaffold00555_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00556_pilon_pilon_mm10.smiCra1.maf > scaffold00556_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00557_pilon_pilon_mm10.smiCra1.maf > scaffold00557_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00558_pilon_pilon_mm10.smiCra1.maf > scaffold00558_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00559_pilon_pilon_mm10.smiCra1.maf > scaffold00559_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00560_pilon_pilon_mm10.smiCra1.maf > scaffold00560_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00561_pilon_pilon_mm10.smiCra1.maf > scaffold00561_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00562_pilon_pilon_mm10.smiCra1.maf > scaffold00562_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00563_pilon_pilon_mm10.smiCra1.maf > scaffold00563_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00564_pilon_pilon_mm10.smiCra1.maf > scaffold00564_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00565_pilon_pilon_mm10.smiCra1.maf > scaffold00565_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00566_pilon_pilon_mm10.smiCra1.maf > scaffold00566_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00567_pilon_pilon_mm10.smiCra1.maf > scaffold00567_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00568_pilon_pilon_mm10.smiCra1.maf > scaffold00568_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00569_pilon_pilon_mm10.smiCra1.maf > scaffold00569_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00570_pilon_pilon_mm10.smiCra1.maf > scaffold00570_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00571_pilon_pilon_mm10.smiCra1.maf > scaffold00571_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00572_pilon_pilon_mm10.smiCra1.maf > scaffold00572_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00573_pilon_pilon_mm10.smiCra1.maf > scaffold00573_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00574_pilon_pilon_mm10.smiCra1.maf > scaffold00574_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00575_pilon_pilon_mm10.smiCra1.maf > scaffold00575_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00576_pilon_pilon_mm10.smiCra1.maf > scaffold00576_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00577_pilon_pilon_mm10.smiCra1.maf > scaffold00577_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00578_pilon_pilon_mm10.smiCra1.maf > scaffold00578_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00579_pilon_pilon_mm10.smiCra1.maf > scaffold00579_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00580_pilon_pilon_mm10.smiCra1.maf > scaffold00580_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00581_pilon_pilon_mm10.smiCra1.maf > scaffold00581_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00582_pilon_pilon_mm10.smiCra1.maf > scaffold00582_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00583_pilon_pilon_mm10.smiCra1.maf > scaffold00583_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00584_pilon_pilon_mm10.smiCra1.maf > scaffold00584_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00585_pilon_pilon_mm10.smiCra1.maf > scaffold00585_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00586_pilon_pilon_mm10.smiCra1.maf > scaffold00586_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00587_pilon_pilon_mm10.smiCra1.maf > scaffold00587_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00588_pilon_pilon_mm10.smiCra1.maf > scaffold00588_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00589_pilon_pilon_mm10.smiCra1.maf > scaffold00589_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00590_pilon_pilon_mm10.smiCra1.maf > scaffold00590_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00591_pilon_pilon_mm10.smiCra1.maf > scaffold00591_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00592_pilon_pilon_mm10.smiCra1.maf > scaffold00592_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00593_pilon_pilon_mm10.smiCra1.maf > scaffold00593_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00594_pilon_pilon_mm10.smiCra1.maf > scaffold00594_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00595_pilon_pilon_mm10.smiCra1.maf > scaffold00595_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00596_pilon_pilon_mm10.smiCra1.maf > scaffold00596_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00597_pilon_pilon_mm10.smiCra1.maf > scaffold00597_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00598_pilon_pilon_mm10.smiCra1.maf > scaffold00598_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00599_pilon_pilon_mm10.smiCra1.maf > scaffold00599_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00600_pilon_pilon_mm10.smiCra1.maf > scaffold00600_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00601_pilon_pilon_mm10.smiCra1.maf > scaffold00601_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00602_pilon_pilon_mm10.smiCra1.maf > scaffold00602_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00603_pilon_pilon_mm10.smiCra1.maf > scaffold00603_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00604_pilon_pilon_mm10.smiCra1.maf > scaffold00604_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00605_pilon_pilon_mm10.smiCra1.maf > scaffold00605_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00606_pilon_pilon_mm10.smiCra1.maf > scaffold00606_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00607_pilon_pilon_mm10.smiCra1.maf > scaffold00607_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00608_pilon_pilon_mm10.smiCra1.maf > scaffold00608_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00609_pilon_pilon_mm10.smiCra1.maf > scaffold00609_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00610_pilon_pilon_mm10.smiCra1.maf > scaffold00610_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00611_pilon_pilon_mm10.smiCra1.maf > scaffold00611_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00612_pilon_pilon_mm10.smiCra1.maf > scaffold00612_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00613_pilon_pilon_mm10.smiCra1.maf > scaffold00613_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00614_pilon_pilon_mm10.smiCra1.maf > scaffold00614_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00615_pilon_pilon_mm10.smiCra1.maf > scaffold00615_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00616_pilon_pilon_mm10.smiCra1.maf > scaffold00616_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00617_pilon_pilon_mm10.smiCra1.maf > scaffold00617_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00618_pilon_pilon_mm10.smiCra1.maf > scaffold00618_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00619_pilon_pilon_mm10.smiCra1.maf > scaffold00619_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00620_pilon_pilon_mm10.smiCra1.maf > scaffold00620_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00621_pilon_pilon_mm10.smiCra1.maf > scaffold00621_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00622_pilon_pilon_mm10.smiCra1.maf > scaffold00622_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00623_pilon_pilon_mm10.smiCra1.maf > scaffold00623_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00624_pilon_pilon_mm10.smiCra1.maf > scaffold00624_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00625_pilon_pilon_mm10.smiCra1.maf > scaffold00625_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00626_pilon_pilon_mm10.smiCra1.maf > scaffold00626_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00627_pilon_pilon_mm10.smiCra1.maf > scaffold00627_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00628_pilon_pilon_mm10.smiCra1.maf > scaffold00628_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00629_pilon_pilon_mm10.smiCra1.maf > scaffold00629_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00630_pilon_pilon_mm10.smiCra1.maf > scaffold00630_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00631_pilon_pilon_mm10.smiCra1.maf > scaffold00631_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00632_pilon_pilon_mm10.smiCra1.maf > scaffold00632_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00633_pilon_pilon_mm10.smiCra1.maf > scaffold00633_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00634_pilon_pilon_mm10.smiCra1.maf > scaffold00634_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00635_pilon_pilon_mm10.smiCra1.maf > scaffold00635_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00636_pilon_pilon_mm10.smiCra1.maf > scaffold00636_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00637_pilon_pilon_mm10.smiCra1.maf > scaffold00637_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00638_pilon_pilon_mm10.smiCra1.maf > scaffold00638_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00639_pilon_pilon_mm10.smiCra1.maf > scaffold00639_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00640_pilon_pilon_mm10.smiCra1.maf > scaffold00640_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00641_pilon_pilon_mm10.smiCra1.maf > scaffold00641_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00642_pilon_pilon_mm10.smiCra1.maf > scaffold00642_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00643_pilon_pilon_mm10.smiCra1.maf > scaffold00643_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00644_pilon_pilon_mm10.smiCra1.maf > scaffold00644_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00645_pilon_pilon_mm10.smiCra1.maf > scaffold00645_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00646_pilon_pilon_mm10.smiCra1.maf > scaffold00646_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00647_pilon_pilon_mm10.smiCra1.maf > scaffold00647_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00648_pilon_pilon_mm10.smiCra1.maf > scaffold00648_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00649_pilon_pilon_mm10.smiCra1.maf > scaffold00649_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00650_pilon_pilon_mm10.smiCra1.maf > scaffold00650_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00651_pilon_pilon_mm10.smiCra1.maf > scaffold00651_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00652_pilon_pilon_mm10.smiCra1.maf > scaffold00652_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00653_pilon_pilon_mm10.smiCra1.maf > scaffold00653_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00654_pilon_pilon_mm10.smiCra1.maf > scaffold00654_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00655_pilon_pilon_mm10.smiCra1.maf > scaffold00655_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00656_pilon_pilon_mm10.smiCra1.maf > scaffold00656_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00657_pilon_pilon_mm10.smiCra1.maf > scaffold00657_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00658_pilon_pilon_mm10.smiCra1.maf > scaffold00658_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00659_pilon_pilon_mm10.smiCra1.maf > scaffold00659_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00660_pilon_pilon_mm10.smiCra1.maf > scaffold00660_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00661_pilon_pilon_mm10.smiCra1.maf > scaffold00661_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00662_pilon_pilon_mm10.smiCra1.maf > scaffold00662_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00663_pilon_pilon_mm10.smiCra1.maf > scaffold00663_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00664_pilon_pilon_mm10.smiCra1.maf > scaffold00664_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00665_pilon_pilon_mm10.smiCra1.maf > scaffold00665_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00666_pilon_pilon_mm10.smiCra1.maf > scaffold00666_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00667_pilon_pilon_mm10.smiCra1.maf > scaffold00667_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00668_pilon_pilon_mm10.smiCra1.maf > scaffold00668_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00669_pilon_pilon_mm10.smiCra1.maf > scaffold00669_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00670_pilon_pilon_mm10.smiCra1.maf > scaffold00670_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00671_pilon_pilon_mm10.smiCra1.maf > scaffold00671_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00672_pilon_pilon_mm10.smiCra1.maf > scaffold00672_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00673_pilon_pilon_mm10.smiCra1.maf > scaffold00673_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00674_pilon_pilon_mm10.smiCra1.maf > scaffold00674_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00675_pilon_pilon_mm10.smiCra1.maf > scaffold00675_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00676_pilon_pilon_mm10.smiCra1.maf > scaffold00676_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00677_pilon_pilon_mm10.smiCra1.maf > scaffold00677_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00678_pilon_pilon_mm10.smiCra1.maf > scaffold00678_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00679_pilon_pilon_mm10.smiCra1.maf > scaffold00679_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00680_pilon_pilon_mm10.smiCra1.maf > scaffold00680_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00681_pilon_pilon_mm10.smiCra1.maf > scaffold00681_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00682_pilon_pilon_mm10.smiCra1.maf > scaffold00682_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00683_pilon_pilon_mm10.smiCra1.maf > scaffold00683_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00684_pilon_pilon_mm10.smiCra1.maf > scaffold00684_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00685_pilon_pilon_mm10.smiCra1.maf > scaffold00685_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00686_pilon_pilon_mm10.smiCra1.maf > scaffold00686_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00687_pilon_pilon_mm10.smiCra1.maf > scaffold00687_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00688_pilon_pilon_mm10.smiCra1.maf > scaffold00688_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00689_pilon_pilon_mm10.smiCra1.maf > scaffold00689_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00690_pilon_pilon_mm10.smiCra1.maf > scaffold00690_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00691_pilon_pilon_mm10.smiCra1.maf > scaffold00691_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00692_pilon_pilon_mm10.smiCra1.maf > scaffold00692_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00693_pilon_pilon_mm10.smiCra1.maf > scaffold00693_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00694_pilon_pilon_mm10.smiCra1.maf > scaffold00694_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00695_pilon_pilon_mm10.smiCra1.maf > scaffold00695_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00696_pilon_pilon_mm10.smiCra1.maf > scaffold00696_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00697_pilon_pilon_mm10.smiCra1.maf > scaffold00697_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00698_pilon_pilon_mm10.smiCra1.maf > scaffold00698_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00699_pilon_pilon_mm10.smiCra1.maf > scaffold00699_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00700_pilon_pilon_mm10.smiCra1.maf > scaffold00700_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00701_pilon_pilon_mm10.smiCra1.maf > scaffold00701_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00702_pilon_pilon_mm10.smiCra1.maf > scaffold00702_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00703_pilon_pilon_mm10.smiCra1.maf > scaffold00703_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00704_pilon_pilon_mm10.smiCra1.maf > scaffold00704_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00705_pilon_pilon_mm10.smiCra1.maf > scaffold00705_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00706_pilon_pilon_mm10.smiCra1.maf > scaffold00706_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00707_pilon_pilon_mm10.smiCra1.maf > scaffold00707_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00708_pilon_pilon_mm10.smiCra1.maf > scaffold00708_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00709_pilon_pilon_mm10.smiCra1.maf > scaffold00709_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00710_pilon_pilon_mm10.smiCra1.maf > scaffold00710_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00711_pilon_pilon_mm10.smiCra1.maf > scaffold00711_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00712_pilon_pilon_mm10.smiCra1.maf > scaffold00712_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00713_pilon_pilon_mm10.smiCra1.maf > scaffold00713_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00714_pilon_pilon_mm10.smiCra1.maf > scaffold00714_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00715_pilon_pilon_mm10.smiCra1.maf > scaffold00715_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00716_pilon_pilon_mm10.smiCra1.maf > scaffold00716_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00717_pilon_pilon_mm10.smiCra1.maf > scaffold00717_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00718_pilon_pilon_mm10.smiCra1.maf > scaffold00718_pilon_pilon_mm10.smiCra1.axt
-maf-convert axt scaffold00719_pilon_pilon_mm10.smiCra1.maf > scaffold00719_pilon_pilon_mm10.smiCra1.axt
diff --git a/cross_species_comparison/scripts/parse_FASTA.py b/cross_species_comparison/scripts/parse_FASTA.py
deleted file mode 100644
index e6bde958eb558d17de38f63fed954520d962d1b4..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/parse_FASTA.py
+++ /dev/null
@@ -1,24 +0,0 @@
-#!usr/bin/env python3
-
-import string
-import random
-import sys
-import os
-from Bio import SeqIO
-
-## usage python3 parse_FASTA.py [input.fasta] > [output.fasta]
-
-file = sys.argv[1]
-
-# Loop through all the files in the variable
-with open(file, 'r') as all_TWARs:
-
-    # for each record (TWAR header) in the file parse it as a FASTA
-    for record in SeqIO.parse(all_TWARs, "fasta"):
-        # set the record ID to a variable
-        id = record.id
-        # set the TWAR sequence to a variable
-        seq = record.seq
-
-        print(">" + str(id) + "\n" + str(seq) + "*")
-all_TWARs.close()
diff --git a/cross_species_comparison/scripts/repeatMasker.sh b/cross_species_comparison/scripts/repeatMasker.sh
deleted file mode 100644
index fc918a4e893c3bb793446ad89dc9eef72bfdf428..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/repeatMasker.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-##!/usr/bin/env bash
-
-## This script loops through all scaffolds in the dunnart genome and
-## generates a separate RepeatMasker command for each scaffold
-## These commands are then used in a slurm array script to run jobs in parallel
-
-TRA=($(for file in *.fa; do echo $file |cut -d "." -f 1;done))
-
-echo ${TRA[@]}
-
-for tr in ${TRA[@]};
-
-do
-
-echo 'RepeatMasker -q -xsmall smiCra1/'${tr}'.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/'
-
-
-done
diff --git a/cross_species_comparison/scripts/repeatMaskerCommands.sh b/cross_species_comparison/scripts/repeatMaskerCommands.sh
deleted file mode 100644
index 90af40bd0f353a725eeaaa0eb00b1ece1dee0fdd..0000000000000000000000000000000000000000
--- a/cross_species_comparison/scripts/repeatMaskerCommands.sh
+++ /dev/null
@@ -1,719 +0,0 @@
-RepeatMasker -q -xsmall smiCra1/scaffold00001_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00002_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00003_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00004_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00005_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00006_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00007_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00008_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00009_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00010_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00011_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00012_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00013_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00014_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00015_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00016_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00017_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00018_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00019_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00020_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00021_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00022_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00023_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00024_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00025_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00026_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
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-RepeatMasker -q -xsmall smiCra1/scaffold00676_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00677_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00678_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00679_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00680_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00681_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00682_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00683_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00684_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00685_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00686_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00687_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00688_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00689_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00690_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00691_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00692_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00693_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00694_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00695_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00696_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00697_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00698_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00699_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00700_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00701_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00702_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00703_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00704_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00705_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00706_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00707_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00708_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00709_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00710_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00711_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00712_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00713_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00714_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00715_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00716_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00717_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00718_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
-RepeatMasker -q -xsmall smiCra1/scaffold00719_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
diff --git a/dunnart/README.md b/dunnart/README.md
old mode 100644
new mode 100755
index 509cdbd67a30dffc3d7bdf8a3b9e9dc982391870..86cbabebc49903076eff598b55f129e6a97d509f
--- a/dunnart/README.md
+++ b/dunnart/README.md
@@ -257,13 +257,17 @@ This was a suggestion of deepTools: https://deeptools.readthedocs.io/en/latest/c
 
 Install khmer program:
 ```{bash eval=FALSE}
-pip3 install khmer
+module load 
+module load 
+pip2.7 install khmer
 ```
 
 Run `unique-kmers.py` on dunnart genome for read length of 150bp:
 
 ```{bash eval=FALSE}
-/usr/local/bin/unique-kmers.py -k 150 dunnart_pseudochr_vs_mSarHar1.11_v1.fa
+/usr/local/bin/unique-kmers.py -k 150 Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fa
+
+/usr/local/bin/unique-kmers.py -k 150 Sminthopsis_crassicaudata_HiC.fasta
 
 ```
 ```
@@ -286,7 +290,9 @@ module load bowtie2/2.3.5.1
 __Build index__
 
 ```{bash eval=FALSE}
-bowtie2-build /data/projects/punim0586/lecook/chip/reference_data/bowtie2/dunnart_pseudochr_vs_mSarHar1.11_v1.fasta
+bowtie2-build Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fa Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr
+
+bowtie2-build Sminthopsis_crassicaudata_HiC.fasta Sminthopsis_crassicaudata_HiC
 ```
 
 #  3. FILTERING
@@ -494,6 +500,16 @@ __Peak calling arguments__
 
 I've left all the shifting model and peak calling arguments as default
 
+## Calling peaks after normalising to 10M reads
+
+This is so we can compare to mouse peaks.
+Based on https://davemcg.github.io/post/easy-bam-downsampling/ script for doing this. 
+
+```
+bash subsample.sh
+```
+
+Run script in the directory with the files you want to subsample.
 
 ### rule call_peaks_macs2:
 
@@ -550,7 +566,14 @@ Overlap replicate peaks for H3K4me3.
 Overlap replicate peaks for H3K27ac.
 
 # 10. Consensus peaks and grouping into putative enhancers and promoters
+## Merge peaks with max distance between peaks of 50bp 
 
+```
+sort -k1,1 -k2,2n H3K4me3_overlap.narrowPeak > H3K4me3_overlap.sorted.narrowPeak
+sort -k1,1 -k2,2n H3K27ac_overlap.narrowPeak > H3K27ac_overlap.sorted.narrowPeak
+bedtools merge -i H3K4me3_overlap.sorted.narrowPeak -d 50 > H3K4me3_overlap_merge.narrowPeak
+bedtools merge -i H3K27ac_overlap.sorted.narrowPeak -d 50 > H3K27ac_overlap_merge.narrowPeak
+```
 
 Find uniquely H3K4me3 sites (i.e. peaks that don't overlap with H3K27ac):
 
@@ -568,7 +591,17 @@ Find peaks common between H3K27ac & H3K4me3 with a reciprocal overlap of at leas
 bedtools intersect -f 0.5 -r -a H3K4me3_overlap.narrowPeak -b H3K27ac_overlap.narrowPeak > H3K4me3_and_H3K27ac.narrowPeak
 ```
 
-# 11. Find overlap with TWARs
+Combine files
+
+```
+cat H3K4me3_only.narrowPeak H3K4me3_and_H3K27ac.narrowPeak | sort -k 4,4 > dunnart_promoter_peaks.narrowPeak
+cat H3K27ac_only.narrowPeak | sort -k 4,4 > dunnart_enhancer_peaks.narrowPeak
+
+sort -k1,1 -k2,2n dunnart_enhancers.narrowPeak | bedtools merge -i stdin -d 50 > dunnart_enhancer_mergedPeaks.narrowPeak
+```
+
+
+# 12. Find overlap with TWARs
 
 ## Get dunnart bed coordinates for TWARs
 
@@ -601,18 +634,23 @@ intersectBed -a H3K4me3_and_H3K27ac.narrowPeak -b dunnart_TWARs.bed > twar_H3K27
 ```
 
 
-# 12. ChIPseeker annotate peaks
+# 12. Annotate Peaks
 
+Figures
 
+Dunnart full peaks
 
 
-# Calling peaks after normalising to 10M reads
+Figure 1:
+•	Show figure comparing the number of active regulatory regions (H3K27ac, H3K4me3&H3K27ac, and H3K4me3) for each group 
+•	Figure of tree showing evolutionary distance between dunnart and mouse
+•	Figure showing what each embryonic mouse stage and dunnart P0 stage actually look like
 
-This is so we can compare to mouse peaks.
-Based on https://davemcg.github.io/post/easy-bam-downsampling/ script for doing this. 
+Figure 2:
+•	Number of enhancers and promoters
+•	Promoter and enhancer activity conserved between the different mouse embryonic stages and dunnart
+•	Distance to nearest TSS
 
-```
-bash subsample.sh
-```
 
-Run script in the directory with the files you want to subsample.
+Dunnart/mouse 10M reads peaks
+
diff --git a/dunnart/Snakefile b/dunnart/Snakefile
old mode 100644
new mode 100755
index eace19d8814e1fb26ef29fddd11e4a759241f60f..6fda8b979ef68b1b5e9f0c7e17d74d87e1cccc0b
--- a/dunnart/Snakefile
+++ b/dunnart/Snakefile
@@ -46,63 +46,63 @@ all_samples = IPS + unique_inputs
 
 rule all:
     input:
-        # expand("results_10M/qc/{sample}_R1_fastqc.html", sample=all_samples),
-        # expand("results_10M/qc/{sample}_R2_fastqc.html", sample=all_samples),
-        # expand("results_10M/qc/{sample}_R1_fastqc.zip", sample=all_samples),
-        # expand("results_10M/qc/{sample}_R2_fastqc.zip", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}.sorted.bam", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}.sorted.bai", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.bam", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai", sample=all_samples),
-        "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
-        "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
-         "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam",
-        "results_10M/logs/H3K4me3.mergeBAM",
-        "results_10M/logs/H3K27ac.mergeBAM",
-        # expand("results_10M/qc/{sample}.unfiltered.flagstat.qc", sample=all_samples),
-        #expand("results_10M/qc/{sample}.dedup.flagstat.qc", sample=all_samples),
-        # expand("results_10M/qc/{sample}.dupmark.flagstat.qc", sample=all_samples),
-        # expand("results_10M/qc/{sample}.PPq30.flagstat.qc", sample=all_samples),
-        #expand("results_10M/qc/{sample}.ccurve.txt", sample=all_samples),
-        # expand("results_10M/qc/{sample}.extrap.txt", sample=all_samples),
-        # expand("results_10M/logs/{sample}.ccurve.preseq", sample=all_samples),
-        # expand("results_10M/logs/{sample}.extrap.preseq", sample=all_samples),
-        # expand("results_10M/qc/{sample}_est_lib_complex_metrics.txt", sample=all_samples),
-        # expand("results_10M/logs/{sample}.picardLibComplexity", sample=all_samples),
-        # expand("results_10M/qc/{sample}.pbc.qc", sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.tmp.bam",sample=all_samples),
-        # expand("results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam",sample=all_samples),
-        "results_10M/qc/multibamsum.npz",
-        "results_10M/qc/multibamsum.tab",
-        "results_10M/qc/pearsoncor_multibamsum.png",
-        "results_10M/qc/pearsoncor_multibamsum_matrix.txt",
-        expand("results_10M/qc/{sample}.SeqDepthNorm.bw", sample=all_samples),
-        "results_10M/qc/multiBAM_fingerprint.png",
-        "results_10M/qc/multiBAM_fingerprint_metrics.txt",
-        "results_10M/qc/multiBAM_fingerprint_rawcounts.txt",
-        "results_10M/qc/bamPEFragmentSize_hist.png",
-        "results_10M/qc/bamPEFragmentSize_rawcounts.tab",
-        # expand("results_10M/bowtie2/{sample}_R1_trimmed_q30.bam", sample=all_samples),
-        # expand("results_10M/logs/{sample}_filt_15Mreads.SE.spp.log", sample=all_samples),
-        # expand("results_10M/qc/{sample}_filt_15Mreads.SE.cc.qc", sample=all_samples),
-        # expand("results_10M/qc/{sample}_filt_15Mreads.SE.cc.plot.pdf", sample=all_samples),
-        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
-        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.xls", zip, case=IPS, control=INPUTS),
-        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_summits.bed", zip, case=IPS, control=INPUTS),
-        #expand("results_10M/qc/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
-        expand("results_10M/bowtie2/{case}.bedpe", case=IPS),
-        expand("results_10M/logs/{case}.bamToBed", case=IPS),
-        expand("results_10M/qc/{case}_vs_{control}.frip_default.txt", case=IPS, control=INPUTS),
-        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak",
-        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak",
-        "results_10M/macs2/H3K4me3_overlap_default.narrowPeak",
-        "results_10M/macs2/H3K27ac_overlap_default.narrowPeak",
-        "results_10M/qc/H3K4me3_overlap_default.frip",
-        "results_10M/qc/H3K27ac_overlap_default.frip"
-        #directory("results_10M/multiqc/multiqc_report_data/"),
-        # "results_10M/multiqc/multiqc_report.html"
+        expand("results/qc/{sample}_R1_fastqc.html", sample=all_samples),
+        expand("results/qc/{sample}_R2_fastqc.html", sample=all_samples),
+        expand("results/qc/{sample}_R1_fastqc.zip", sample=all_samples),
+        expand("results/qc/{sample}_R2_fastqc.zip", sample=all_samples),
+        expand("results/bowtie2/{sample}.HiC.sorted.bam", sample=all_samples),
+        expand("results/bowtie2/{sample}.HiC.sorted.bai", sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.bam", sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam", sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam", sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai", sample=all_samples),
+        "results/bowtie2/H3K4me3_pooled_PPq30.HiC.sorted.dedup.bam",
+        "results/bowtie2/H3K27ac_pooled_PPq30.HiC.sorted.dedup.bam",
+        "results/bowtie2/input_pooled_PPq30.HiC.sorted.dedup.bam",
+        "results/logsHiC/H3K4me3.mergeBAM",
+        "results/logsHiC/H3K27ac.mergeBAM",
+        expand("results/qc/{sample}.HiC.unfiltered.flagstat.qc", sample=all_samples),
+        expand("results/qc/{sample}.HiC.dedup.flagstat.qc", sample=all_samples),
+        expand("results/qc/{sample}.HiC.dupmark.flagstat.qc", sample=all_samples),
+        expand("results/qc/{sample}.HiC.PPq30.flagstat.qc", sample=all_samples),
+        expand("results/qc/{sample}.HiC.ccurve.txt", sample=all_samples),
+        expand("results/qc/{sample}.HiC.extrap.txt", sample=all_samples),
+        expand("results/logsHiC/{sample}.ccurve.preseq", sample=all_samples),
+        expand("results/logsHiC/{sample}.extrap.preseq", sample=all_samples),
+        # expand("results/qc/{sample}.HiC_est_lib_complex_metrics.txt", sample=all_samples),
+        # expand("results/logsHiC/{sample}.picardLibComplexity", sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.tmp.bam",sample=all_samples),
+        expand("results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.bam",sample=all_samples),
+        expand("results/qc/{sample}.HiC.pbc.qc", sample=all_samples),
+        "results/qc/multibamsum.HiC.npz",
+        "results/qc/multibamsum.HiC.tab",
+        "results/qc/pearsoncor_multibamsum.HiC.png",
+        "results/qc/pearsoncor_multibamsum.HiC_matrix.txt",
+        expand("results/qc/{sample}.HiC.SeqDepthNorm.bw", sample=all_samples),
+        "results/qc/multiBAM.HiC_fingerprint.png",
+        "results/qc/multiBAM.HiC_fingerprint_metrics.txt",
+        "results/qc/multiBAM.HiC_fingerprint_rawcounts.txt",
+        "results/qc/bamPEFragmentSize.HiC_hist.png",
+        "results/qc/bamPEFragmentSize.HiC_rawcounts.tab",
+        expand("results/bowtie2/{sample}.HiC_R1_trimmed_q30.bam", sample=all_samples),
+        expand("results/logsHiC/{sample}_filt_15Mreads.SE.spp.log", sample=all_samples),
+        expand("results/qc/{sample}.HiC_filt_15Mreads.SE.cc.qc", sample=all_samples),
+        expand("results/qc/{sample}.HiC_filt_15Mreads.SE.cc.plot.pdf", sample=all_samples),
+        expand("results/macs2/{case}_vs_{control}_macs2.HiC_default_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
+        expand("results/macs2/{case}_vs_{control}_macs2.HiC_default_peaks.xls", zip, case=IPS, control=INPUTS),
+        expand("results/macs2/{case}_vs_{control}_macs2.HiC_default_summits.bed", zip, case=IPS, control=INPUTS),
+        # expand("results/qc/{case}-vs-{control}-narrowpeak-count_mqc.HiC.json", zip, case=IPS, control=INPUTS),
+        # expand("results/bowtie2/{case}.HiC.bedpe", case=IPS),
+        # expand("results/logsHiC/{case}.HiC.bamToBed", case=IPS),
+        # expand("results/qc/{case}_vs_{control}.frip_default.HiC.txt", case=IPS, control=INPUTS),
+        # "results/macs2/H3K4me3_pooled_macs2_default_peaks.HiC.narrowPeak",
+        # "results/macs2/H3K27ac_pooled_macs2_default_peaks.HiC.narrowPeak",
+        # "results/macs2/H3K4me3_overlap_default.HiC.narrowPeak",
+        # "results/macs2/H3K27ac_overlap_default.HiC.narrowPeak",
+        # "results/qc/H3K4me3_overlap_default.HiC.frip",
+        # "results/qc/H3K27ac_overlap_default.HiC.frip"
+        # directory("results/multiqc/multiqc_report_data/")
+        # "results/multiqc/multiqc_report.html"
 # ===============================================================================================
 #  1. FASTQC
 # ===============================================================================================
@@ -111,14 +111,14 @@ rule fastqc:
     input:
         ["rawdata/{sample}_R1.fastq.gz", "rawdata/{sample}_R2.fastq.gz"]
     output:
-        "results_10M/qc/{sample}_R1_fastqc.html",
-        "results_10M/qc/{sample}_R2_fastqc.html",
-        "results_10M/qc/{sample}_R1_fastqc.zip",
-        "results_10M/qc/{sample}_R2_fastqc.zip"
+        "results/qc/{sample}_R1_fastqc.html",
+        "results/qc/{sample}_R2_fastqc.html",
+        "results/qc/{sample}_R1_fastqc.zip",
+        "results/qc/{sample}_R2_fastqc.zip"
     log:
-        "results_10M/logs/{sample}.fastqc"
+        "results/logsHiC/{sample}.fastqc"
     shell:
-        "fastqc {input} -t 6 --extract --outdir=results_10M/qc/ 2> {log}"
+        "fastqc {input} -t 6 --extract --outdir=results/qc/ 2> {log}"
 
 # ===============================================================================================
 #  2. ALIGNMENT
@@ -130,11 +130,11 @@ rule align:
         R1="rawdata/{sample}_R1.fastq.gz",
         R2="rawdata/{sample}_R2.fastq.gz"
     output:
-        "results_10M/bowtie2/{sample}.sorted.bam"
+        "results/bowtie2/{sample}.HiC.sorted.bam"
     params:
-        index="genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr"
+        index="genomes/Sminthopsis_crassicaudata_HiC"
     log:
-        "results_10M/logs/{sample}.align"
+        "results/logsHiC/{sample}.align"
     shell:
         "bowtie2 --threads 8 -q -X 2000 --very-sensitive -x {params.index} -1 {input.R1} -2 {input.R2} \
         |  samtools view -u -h  - |  samtools sort -o {output}  - 2> {log}"
@@ -157,59 +157,59 @@ rule align:
 
 rule filter:
     input:
-        "results_10M/bowtie2/{sample}.sorted.bam"
+        "results/bowtie2/{sample}.HiC.sorted.bam"
     output:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.bam"
     log:
-        "results_10M/logs/{sample}.dedup"
+        "results/logsHiC/{sample}.dedup"
     shell:
         "samtools view -b -F 1804 -q 30 -f 2 {input} | samtools sort -o {output} - 2> {log}"
 
 rule markDups:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.bam"
     output:
-        bam="results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam",
-        dupQC="results_10M/bowtie2/{sample}.dup.qc"
+        bam="results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam",
+        dupQC="results/bowtie2/{sample}.HiC.dup.qc"
     log:
-        "results_10M/logs/{sample}.dupmark"
+        "results/logsHiC/{sample}.dupmark"
     shell:
         "picard MarkDuplicates I={input} O={output.bam} \
         METRICS_FILE={output.dupQC} REMOVE_DUPLICATES=FALSE ASSUME_SORTED=true 2> {log}"
 
 rule dedup:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam"
     output:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam"
     log:
-        "results_10M/logs/{sample}.dedup"
+        "results/logsHiC/{sample}.dedup"
     shell:
         "samtools view -F 1804 -f 2 -b {input} | samtools sort -o {output} - 2> {log}"
 
 rule indexBam:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam"
     output:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai"
     log:
-        "results_10M/logs/{sample}.indexBam"
+        "results/logsHiC/{sample}.indexBam"
     shell:
         "samtools index -c {input} {output} 2> {log}"
 
 rule mergeBAMreplicates:
     input:
-        H3K4me3 = ["results_10M/bowtie2/A-2_H3K4me3_PPq30.sorted.dedup.bam",  "results_10M/bowtie2/B-2_H3K4me3_PPq30.sorted.dedup.bam"],
-        H3K27ac = ["results_10M/bowtie2/A-3_H3K27ac_PPq30.sorted.dedup.bam", "results_10M/bowtie2/B-3_H3K27ac_PPq30.sorted.dedup.bam"],
-        control = ["results_10M/bowtie2/A-1_input_PPq30.sorted.dedup.bam", "results_10M/bowtie2/B-1_input_PPq30.sorted.dedup.bam"]
+        H3K4me3 = ["results/bowtie2/A-2_H3K4me3_PPq30.HiC.sorted.dedup.bam",  "results/bowtie2/B-2_H3K4me3_PPq30.HiC.sorted.dedup.bam"],
+        H3K27ac = ["results/bowtie2/A-3_H3K27ac_PPq30.HiC.sorted.dedup.bam", "results/bowtie2/B-3_H3K27ac_PPq30.HiC.sorted.dedup.bam"],
+        control = ["results/bowtie2/A-1_input_PPq30.HiC.sorted.dedup.bam", "results/bowtie2/B-1_input_PPq30.HiC.sorted.dedup.bam"]
     output:
-        H3K4me3 = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
-        H3K27ac = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
-        control = "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam"
+        H3K4me3 = "results/bowtie2/H3K4me3_pooled_PPq30.HiC.sorted.dedup.bam",
+        H3K27ac = "results/bowtie2/H3K27ac_pooled_PPq30.HiC.sorted.dedup.bam",
+        control = "results/bowtie2/input_pooled_PPq30.HiC.sorted.dedup.bam"
     log:
-        H3K4me3 = "results_10M/logs/H3K4me3.mergeBAM",
-        H3K27ac = "results_10M/logs/H3K27ac.mergeBAM",
-        control = "results_10M/logs/input.mergeBAM"
+        H3K4me3 = "results/logsHiC/H3K4me3.mergeBAM",
+        H3K27ac = "results/logsHiC/H3K27ac.mergeBAM",
+        control = "results/logsHiC/input.mergeBAM"
     run:
         shell("samtools merge {output.H3K4me3} {input.H3K4me3} 2> {log.H3K4me3}")
         shell("samtools merge {output.H3K27ac} {input.H3K27ac} 2> {log.H3K27ac}")
@@ -225,15 +225,15 @@ rule mergeBAMreplicates:
 
 rule mappingStats:
     input:
-        a="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam",
-        b="results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam",
-        c="results_10M/bowtie2/{sample}_PPq30.sorted.bam",
-        d="results_10M/bowtie2/{sample}.sorted.bam"
+        a="results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam",
+        b="results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam",
+        c="results/bowtie2/{sample}_PPq30.HiC.sorted.bam",
+        d="results/bowtie2/{sample}.HiC.sorted.bam"
     output:
-        a="results_10M/qc/{sample}.dedup.flagstat.qc",
-        b="results_10M/qc/{sample}.dupmark.flagstat.qc",
-        c="results_10M/qc/{sample}.PPq30.flagstat.qc",
-        d="results_10M/qc/{sample}.unfiltered.flagstat.qc",
+        a="results/qc/{sample}.HiC.dedup.flagstat.qc",
+        b="results/qc/{sample}.HiC.dupmark.flagstat.qc",
+        c="results/qc/{sample}.HiC.PPq30.flagstat.qc",
+        d="results/qc/{sample}.HiC.unfiltered.flagstat.qc",
     run:
         shell("samtools flagstat {input.a} > {output.a}")
         shell("samtools flagstat {input.b} > {output.b}")
@@ -243,55 +243,55 @@ rule mappingStats:
 
 rule downsample_bam:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam"
     output:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.bam"
     log:
-        "results_10M/logs/{sample}.downsample"
+        "results/logsHiC/{sample}.downsample"
     shell:
         "picard DownsampleSam I={input} O={output} P=0.35 \
          2> {log}"
 
 rule preseq:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam"
     output:
-        ccurve = "results_10M/qc/{sample}.ccurve.txt",
-        extrap = "results_10M/qc/{sample}.extrap.txt"
+        ccurve = "results/qc/{sample}.HiC.ccurve.txt",
+        extrap = "results/qc/{sample}.HiC.extrap.txt"
     log:
-        ccurve = "results_10M/logs/{sample}.ccurve.preseq",
-        extrap = "results_10M/logs/{sample}.extrap.preseq"
+        ccurve = "results/logsHiC/{sample}.ccurve.preseq",
+        extrap = "results/logsHiC/{sample}.extrap.preseq"
     run:
         shell("preseq lc_extrap -v -output {output.extrap} -pe -bam {input} 2> {log.extrap}")
         shell("preseq c_curve -v -output {output.ccurve} -pe -bam {input} 2> {log.ccurve}")
 
 rule get_picard_complexity_metrics:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark.bam"
     output:
-        "results_10M/qc/{sample}_est_lib_complex_metrics.txt"
+        "results/qc/{sample}.HiC_est_lib_complex_metrics.txt"
     log:
-        "results_10M/logs/{sample}.downSampled.picardLibComplexity"
+        "results/logsHiC/{sample}.downSampled.picardLibComplexity"
     shell:
         "picard -Xmx6G EstimateLibraryComplexity INPUT={input} OUTPUT={output} USE_JDK_DEFLATER=TRUE USE_JDK_INFLATER=TRUE VERBOSITY=ERROR"
 
 rule sort_name:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.bam"
     output:
-        tmp = "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.tmp.bam"
+        tmp = "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.tmp.bam"
     log:
-        "results_10M/logs/{sample}.pbc.sort"
+        "results/logsHiC/{sample}.pbc.sort"
     run:
         shell("samtools sort -n {input} -o {output.tmp} 2> {log}")
 
 rule estimate_lib_complexity:
     input:
-        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.tmp.bam"
+        "results/bowtie2/{sample}_PPq30.HiC.sorted.dupmark_downSampled.tmp.bam"
     output:
-        qc = "results_10M/qc/{sample}.pbc.qc",
+        qc = "results/qc/{sample}.HiC.pbc.qc",
     log:
-        "results_10M/logs/{sample}.pbc"
+        "results/logsHiC/{sample}.pbc"
     shell:
         """
         bedtools bamtobed -bedpe -i {input} \
@@ -321,14 +321,14 @@ rule estimate_lib_complexity:
 
 rule deeptools_summary:
     input:
-        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
-        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+        bam = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai"], sample=all_samples)
     output:
-        sum="results_10M/qc/multibamsum.npz",
-        counts="results_10M/qc/multibamsum.tab"
+        sum="results/qc/multibamsum.HiC.npz",
+        counts="results/qc/multibamsum.HiC.tab"
     threads: 32
     log:
-        "results_10M/logs/multisummary.deepTools"
+        "results/logsHiC/multisummary.deepTools"
     shell:
         " multiBamSummary bins \
         -p {threads} \
@@ -338,12 +338,12 @@ rule deeptools_summary:
         --outRawCounts {output.counts} 2> {log}"
 
 rule deeptools_correlation:
-    input: "results_10M/qc/multibamsum.npz"
+    input: "results/qc/multibamsum.HiC.npz"
     output:
-        fig="results_10M/qc/pearsoncor_multibamsum.png",
-        matrix="results_10M/qc/pearsoncor_multibamsum_matrix.txt"
+        fig="results/qc/pearsoncor_multibamsum.HiC.png",
+        matrix="results/qc/pearsoncor_multibamsum.HiC_matrix.txt"
     log:
-        "results_10M/logs/correlation.deepTools"
+        "results/logsHiC/correlation.deepTools"
     shell:
         "plotCorrelation \
         --corData {input} \
@@ -353,16 +353,16 @@ rule deeptools_correlation:
         --whatToPlot heatmap \
         --skipZeros \
         --plotNumbers \
-        --colorMap RdYlBu 2> {log}"
+        --colorMap #264653 #2A9D8F #E9C46A #F4A261 #E76F51 2> {log}"
 
 rule deeptools_coverage:
     input:
-        bam ="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam",
-        bai ="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"
+        bam ="results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam",
+        bai ="results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai"
     output:
-        "results_10M/qc/{sample}.SeqDepthNorm.bw"
+        "results/qc/{sample}.HiC.SeqDepthNorm.bw"
     log:
-        "results_10M/logs/{sample}_coverage.deepTools"
+        "results/logsHiC/{sample}_coverage.deepTools"
     shell:
         "bamCoverage \
         --bam {input.bam} \
@@ -370,19 +370,21 @@ rule deeptools_coverage:
         --normalizeUsing RPGC \
         --effectiveGenomeSize 2740338543 \
         --extendReads \
+        --legendLocation none
+        --colors #264653 #2A9D8F #E9C46A #F4A261 #E76F51
         -o {output} 2> {log}"
 
 rule deeptools_fingerprint:
     input:
-        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
-        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+        bam = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai"], sample=all_samples)
     output:
-        fig="results_10M/qc/multiBAM_fingerprint.png",
-        metrics="results_10M/qc/multiBAM_fingerprint_metrics.txt",
-        rawcounts="results_10M/qc/multiBAM_fingerprint_rawcounts.txt"
+        fig="results/qc/multiBAM.HiC_fingerprint.png",
+        metrics="results/qc/multiBAM.HiC_fingerprint_metrics.txt",
+        rawcounts="results/qc/multiBAM.HiC_fingerprint_rawcounts.txt"
     threads: 32
     log:
-        "results_10M/logs/fingerprint.deepTools"
+        "results/logsHiC/fingerprint.deepTools"
     shell:
         "plotFingerprint -p {threads} \
         -b {input.bam} \
@@ -395,19 +397,23 @@ rule deeptools_fingerprint:
 
 rule deeptools_bamPEFragmentSize:
     input:
-        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
-        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+        bam = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results/bowtie2/{sample}_PPq30.HiC.sorted.dedup.bai"], sample=all_samples)
     output:
-        fig="results_10M/qc/bamPEFragmentSize_hist.png",
-        rawcounts="results_10M/qc/bamPEFragmentSize_rawcounts.tab"
+        fig="results/qc/bamPEFragmentSize.HiC_hist.png",
+        rawcounts="results/qc/bamPEFragmentSize.HiC_rawcounts.tab"
     log:
-        "results_10M/logs/bamPEFragmentSize.deepTools"
+        "results/logsHiC/bamPEFragmentSize.deepTools"
     shell:
         "bamPEFragmentSize \
         -hist {output.fig} \
         --outRawFragmentLengths {output.rawcounts} \
         -b {input.bam} 2> {log}"
 
+rule deeptools_computeGCBias:
+
+computeGCBias -b {input.bam} --effectiveGenomeSize 2740338543
+   --genome ../../../cross_species/data/genomes/smiCra1.2bit  -freq freq_test.txt --biasPlot test_gc.pdf
 
 # ===============================================================================================
 #  6. Cross-correlation scores - following ENCODE code
@@ -420,7 +426,7 @@ rule trim_read1:
     input:
         "rawdata/{sample}_R1.fastq.gz"
     output:
-        "results_10M/qc/{sample}_R1_trimmed.fastq.gz"
+        "results/qc/{sample}_R1_trimmed.fastq.gz"
     run:
         shell("python scripts/trimfastq.py {input} 50 | gzip -nc > {output}")
 
@@ -428,13 +434,13 @@ rule trim_read1:
 
 rule align_trimmed_read1:
     input:
-        "results_10M/qc/{sample}_R1_trimmed.fastq.gz"
+        "results/qc/{sample}_R1_trimmed.fastq.gz"
     output:
-        "results_10M/bowtie2/{sample}_R1_trimmed.bam"
+        "results/bowtie2/{sample}_R1_trimmed.bam"
     params:
         index="genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr"
     log:
-        "results_10M/logs/{sample}_align_trimmed_read1.log"
+        "results/logsHiC/{sample}_align_trimmed_read1.log"
     shell:
         "bowtie2 -x {params.index} -U {input} 2> {log} | \
         samtools view -Su - | samtools sort -o {output} - 2> {log}"
@@ -443,19 +449,19 @@ rule align_trimmed_read1:
 
 rule filter_sort_trimmed_alignment:
     input:
-        "results_10M/bowtie2/{sample}_R1_trimmed.bam"
+        "results/bowtie2/{sample}_R1_trimmed.bam"
     output:
-        bam = "results_10M/bowtie2/{sample}_R1_trimmed_q30.bam"
+        bam = "results/bowtie2/{sample}.HiC_R1_trimmed_q30.bam"
     log:
-        "results_10M/logs/{sample}_align_trimmed_read1_filter.log"
+        "results/logsHiC/{sample}_align_trimmed_read1_filter.log"
     run:
         shell("samtools view -F 1804 -q 30 -b {input} -o {output.bam}")
 
 rule bamtobed_crossC:
     input:
-        "results_10M/bowtie2/{sample}_R1_trimmed_q30.bam"
+        "results/bowtie2/{sample}.HiC_R1_trimmed_q30.bam"
     output:
-        tagAlign = "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
+        tagAlign = "results/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
     shell:
         """
         bedtools bamtobed -i {input} | \
@@ -467,10 +473,10 @@ rule bamtobed_crossC:
 ## Estimate read length from first 100 reads
 rule subsample_aligned_reads:
     input:
-        "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
+        "results/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
     output:
-        subsample = "results_10M/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz",
-        tmp = "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.tmp"
+        subsample = "results/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz",
+        tmp = "results/bed/{sample}_R1_trimmed_q30_SE.tagAlign.tmp"
     params:
         nreads= 15000000
     run:
@@ -488,12 +494,12 @@ rule subsample_aligned_reads:
 
 rule cross_correlation_SSP:
     input:
-        "results_10M/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz"
+        "results/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz"
     output:
-        CC_SCORES_FILE="results_10M/qc/{sample}_filt_15Mreads.SE.cc.qc",
-        CC_PLOT_FILE="results_10M/qc/{sample}_filt_15Mreads.SE.cc.plot.pdf"
+        CC_SCORES_FILE="results/qc/{sample}.HiC_filt_15Mreads.SE.cc.qc",
+        CC_PLOT_FILE="results/qc/{sample}.HiC_filt_15Mreads.SE.cc.plot.pdf"
     log:
-        "results_10M/logs/{sample}_filt_15Mreads.SE.spp.log"
+        "results/logsHiC/{sample}_filt_15Mreads.SE.spp.log"
     params:
         EXCLUSION_RANGE_MIN=-500,
         EXCLUSION_RANGE_MAX=60
@@ -514,50 +520,50 @@ rule cross_correlation_SSP:
 
 rule call_peaks_macs2:
     input:
-        control = "results_10M/bowtie2/{control}_PPq30.sorted.dedup.bam",
-        case = "results_10M/bowtie2/{case}_PPq30.sorted.dedup.bam"
+        control = "results/bowtie2/{control}_PPq30.HiC.sorted.dedup.bam",
+        case = "results/bowtie2/{case}_PPq30.HiC.sorted.dedup.bam"
     output:
-        "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.xls",
-        "results_10M/macs2/{case}_vs_{control}_macs2_default_summits.bed",
-        "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak",
+        "results/macs2/{case}_vs_{control}_macs2.HiC_default_peaks.xls",
+        "results/macs2/{case}_vs_{control}_macs2.HiC_default_summits.bed",
+        "results/macs2/{case}_vs_{control}_macs2.HiC_default_peaks.narrowPeak",
     log:
-        "results_10M/logs/{case}_vs_{control}_call_peaks_macs2_default.log"
+        "results/logsHiC/{case}_vs_{control}_call_peaks_macs2_default.log"
     params:
         name = "{case}_vs_{control}_macs2_default",
     shell:
         " macs2 callpeak -f BAMPE -t {input.case} \
         -c {input.control} --keep-dup all \
-        --outdir results_10M/macs2/  \
+        --outdir results/macs2/  \
         -n {params.name} \
         -g 2740338543 2> {log} "
 
 rule call_peaks_macs2_pooled_replicates:
     input:
-        H3K4me3 = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
-        H3K27ac = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
-        input = "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam"
+        H3K4me3 = "results/bowtie2/H3K4me3_pooled_PPq30.HiC.sorted.dedup.bam",
+        H3K27ac = "results/bowtie2/H3K27ac_pooled_PPq30.HiC.sorted.dedup.bam",
+        input = "results/bowtie2/input_pooled_PPq30.HiC.sorted.dedup.bam"
     output:
-        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.xls",
-        "results_10M/macs2/H3K4me3_pooled_macs2_default_summits.bed",
-        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak",
-        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.xls",
-        "results_10M/macs2/H3K27ac_pooled_macs2_default_summits.bed",
-        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak"
+        "results/macs2/H3K4me3_pooled_macs2_default_peaks.xls",
+        "results/macs2/H3K4me3_pooled_macs2_default_summits.bed",
+        "results/macs2/H3K4me3_pooled_macs2_default_peaks.HiC.narrowPeak",
+        "results/macs2/H3K27ac_pooled_macs2_default_peaks.xls",
+        "results/macs2/H3K27ac_pooled_macs2_default_summits.bed",
+        "results/macs2/H3K27ac_pooled_macs2_default_peaks.HiC.narrowPeak"
     log:
-        H3K4me3 ="results_10M/logs/H3K4me3_pooled_call_peaks_macs2_default.log",
-        H3K27ac ="results_10M/logs/H3K27ac_pooled_call_peaks_macs2_default.log"
+        H3K4me3 ="results/logsHiC/H3K4me3_pooled_call_peaks_macs2_default.log",
+        H3K27ac ="results/logsHiC/H3K27ac_pooled_call_peaks_macs2_default.log"
     params:
         H3K4me3 = "H3K4me3_pooled_macs2_default",
         H3K27ac = "H3K27ac_pooled_macs2_default"
     run:
         shell(" macs2 callpeak -f BAMPE -t {input.H3K4me3} \
         -c {input.input} --keep-dup all \
-        --outdir results_10M/macs2/ \
+        --outdir results/macs2/ \
         -n {params.H3K4me3} \
         -g 2740338543 2> {log.H3K4me3} ")
         shell("macs2 callpeak -f BAMPE -t {input.H3K27ac} \
         -c {input.input} --keep-dup all \
-        --outdir results_10M/macs2/ -p 0.01 \
+        --outdir results/macs2/ -p 0.01 \
         -n {params.H3K27ac} \
         -g 2740338543 2> {log.H3K27ac} ")
 
@@ -570,9 +576,9 @@ rule call_peaks_macs2_pooled_replicates:
 # peak counts in a format that multiqc can handle
 rule get_narrow_peak_counts_for_multiqc:
     input:
-        peaks = "results_10M/macs2/{case}_vs_{control}_macs2_peaks.narrowPeak"
+        peaks = "results/macs2/{case}_vs_{control}_macs2.HiC_peaks.narrowPeak"
     output:
-        "results_10M/qc/{case}-vs-{control}-narrowpeak-count_mqc.json"
+        "results/qc/{case}-vs-{control}-narrowpeak-count_mqc.HiC.json"
     params:
         peakType = "narrowPeak"
     shell:
@@ -584,11 +590,11 @@ rule get_narrow_peak_counts_for_multiqc:
 ## Convert BAM to tagAlign file for calculating FRiP QC metric (Fraction of reads in peaks)
 rule bamToBed:
     input:
-        "results_10M/bowtie2/{case}_PPq30.sorted.dedup.bam"
+        "results/bowtie2/{case}_PPq30.HiC.sorted.dedup.bam"
     output:
-        "results_10M/bowtie2/{case}.bedpe"
+        "results/bowtie2/{case}.HiC.bedpe"
     log:
-        "results_10M/logs/{case}.bamToBed"
+        "results/logsHiC/{case}.bamToBed"
     shell:
         "samtools sort -n {input} | bedtools bamtobed -bedpe -mate1 -i - > {output}"
 
@@ -596,10 +602,10 @@ rule bamToBed:
 ## Fraction of reads in peaks
 rule frip:
     input:
-        bed = "results_10M/bowtie2/{case}.bedpe",
-        peak = "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak"
+        bed = "results/bowtie2/{case}.HiC.bedpe",
+        peak = "results/macs2/{case}_vs_{control}_macs2.HiC_default_peaks.narrowPeak"
     output:
-        "results_10M/qc/{case}_vs_{control}.frip_default.txt"
+        "results/qc/{case}_vs_{control}.frip_default.HiC.txt"
     shell:
         "python2.7 scripts/encode_frip.py {input.bed} {input.peak} > {output}"
 
@@ -613,84 +619,84 @@ rule frip:
 
 rule overlap_peaks_H3K4me3:
     input:
-        peak1="results_10M/macs2/A-2_H3K4me3_vs_A-1_input_macs2_default_peaks.narrowPeak",
-        peak2="results_10M/macs2/B-2_H3K4me3_vs_B-1_input_macs2_default_peaks.narrowPeak",
-        pooled="results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak"
+        peak1="results/macs2/A-2_H3K4me3_vs_A-1_input_macs2_default_peaks.narrowPeak",
+        peak2="results/macs2/B-2_H3K4me3_vs_B-1_input_macs2_default_peaks.narrowPeak",
+        pooled="results/macs2/H3K4me3_pooled_macs2_default_peaks.HiC.narrowPeak"
     output:
-        "results_10M/macs2/H3K4me3_overlap_default.narrowPeak"
+        "results/macs2/H3K4me3_overlap_default.HiC.narrowPeak"
     shell:
         "python2.7 scripts/overlap_peaks.py {input.peak1} {input.peak2} {input.pooled} {output}"
 
 
 rule overlap_peaks_H3K27ac:
     input:
-        peak1="results_10M/macs2/A-3_H3K27ac_vs_A-1_input_macs2_default_peaks.narrowPeak",
-        peak2="results_10M/macs2/B-3_H3K27ac_vs_B-1_input_macs2_default_peaks.narrowPeak",
-        pooled="results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak"
+        peak1="results/macs2/A-3_H3K27ac_vs_A-1_input_macs2_default_peaks.narrowPeak",
+        peak2="results/macs2/B-3_H3K27ac_vs_B-1_input_macs2_default_peaks.narrowPeak",
+        pooled="results/macs2/H3K27ac_pooled_macs2_default_peaks.HiC.narrowPeak"
     output:
-        "results_10M/macs2/H3K27ac_overlap_default.narrowPeak"
+        "results/macs2/H3K27ac_overlap_default.HiC.narrowPeak"
     shell:
         "python2.7 scripts/overlap_peaks.py {input.peak1} {input.peak2} {input.pooled} {output}"
 
 ## Fraction of reads in peaks
 rule overlap_frip:
     input:
-        H3K4me3bam = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
-        H3K27acbam = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
-        H3K4me3bed = "results_10M/macs2/H3K4me3_overlap_default.narrowPeak",
-        H3K27acbed = "results_10M/macs2/H3K27ac_overlap_default.narrowPeak"
+        H3K4me3bam = "results/bowtie2/H3K4me3_pooled_PPq30.HiC.sorted.dedup.bam",
+        H3K27acbam = "results/bowtie2/H3K27ac_pooled_PPq30.HiC.sorted.dedup.bam",
+        H3K4me3bed = "results/macs2/H3K4me3_overlap_default.HiC.narrowPeak",
+        H3K27acbed = "results/macs2/H3K27ac_overlap_default.HiC.narrowPeak"
     output:
-        H3K4me3frip = "results_10M/qc/H3K4me3_overlap_default.frip",
-        H3K27acfrip = "results_10M/qc/H3K27ac_overlap_default.frip"
+        H3K4me3frip = "results/qc/H3K4me3_overlap_default.HiC.frip",
+        H3K27acfrip = "results/qc/H3K27ac_overlap_default.HiC.frip"
     run:
         shell("python2.7 scripts/encode_frip.py {input.H3K4me3bam} {input.H3K4me3bed} > {output.H3K4me3frip}")
         shell("python2.7 scripts/encode_frip.py {input.H3K27acbam} {input.H3K27acbed} > {output.H3K27acfrip}")
 
-tyui#  10. Combine all QC into multiqc output
+#  10. Combine all QC into multiqc output
 # ===============================================================================================
 #
 # rule multiqc:
 #     input:
 #         # fastqc
-#         expand("results_10M/qc/{sample}_R1_fastqc.html", sample=all_samples),
-#         expand("results_10M/qc/{sample}_R2_fastqc.html", sample=all_samples),
-#         expand("results_10M/qc/{sample}_R1_fastqc.zip", sample=all_samples),
-#         expand("results_10M/qc/{sample}_R2_fastqc.zip", sample=all_samples),
+#         expand("results/qc/{sample}_R1_fastqc.html", sample=all_samples),
+#         expand("results/qc/{sample}_R2_fastqc.html", sample=all_samples),
+#         expand("results/qc/{sample}_R1_fastqc.zip", sample=all_samples),
+#         expand("results/qc/{sample}_R2_fastqc.zip", sample=all_samples),
 #         # bowtie2
-#         expand("results_10M/logs/{sample}.align", sample=all_samples),
-#         expand("results_10M/qc/{sample}.flagstat.qc", sample=all_samples),
+#         expand("results/logsHiC/{sample}.align", sample=all_samples),
+#         expand("results/qc/{sample}.HiC.flagstat.qc", sample=all_samples),
 #         # preseq
-#         expand("results_10M/qc/{sample}.ccurve.txt", sample=all_samples),
-#         expand("results_10M/qc/{sample}.extrap.txt", sample=all_samples),
+#         expand("results/qc/{sample}.HiC.ccurve.txt", sample=all_samples),
+#         expand("results/qc/{sample}.HiC.extrap.txt", sample=all_samples),
 #         # deepTools
-#         "results_10M/deeptools/multibamsum.npz",
-#         "results_10M/deeptools/multibamsum.tab",
-#         "results_10M/deeptools/pearsoncor_multibamsum.png",
-#         "results_10M/deeptools/pearsoncor_multibamsum_matrix.txt",
-#         expand("results_10M/deeptools/{sample}.SeqDepthNorm.bw", sample=all_samples),
-#         "results_10M/deeptools/multiBAM_fingerprint.png",
-#         "results_10M/deeptools/multiBAM_fingerprint_metrics.txt",
-#         "results_10M/deeptools/multiBAM_fingerprint_rawcounts.txt",
-#         "results_10M/deeptools/plot_coverage.png",
-#         "results_10M/deeptools/plot_coverage_rawcounts.tab",
-#         "results_10M/deeptools/bamPEFragmentSize_hist.png",
-#         "results_10M/deeptools/bamPEFragmentSize_rawcounts.tab",
+#         "results/deeptools/multibamsum.npz",
+#         "results/deeptools/multibamsum.tab",
+#         "results/deeptools/pearsoncor_multibamsum.png",
+#         "results/deeptools/pearsoncor_multibamsum_matrix.txt",
+#         expand("results/deeptools/{sample}.SeqDepthNorm.bw", sample=all_samples),
+#         "results/deeptools/multiBAM_fingerprint.png",
+#         "results/deeptools/multiBAM_fingerprint_metrics.txt",
+#         "results/deeptools/multiBAM_fingerprint_rawcounts.txt",
+#         "results/deeptools/plot_coverage.png",
+#         "results/deeptools/plot_coverage_rawcounts.tab",
+#         "results/deeptools/bamPEFragmentSize_hist.png",
+#         "results/deeptools/bamPEFragmentSize_rawcounts.tab",
 #         # phantomPeaks
-#         expand("results_10M/phantomPeaks/{sample}.spp.pdf", sample = IPS),
-#         expand("results_10M/phantomPeaks/{sample}.spp.Rdata", sample = IPS),
-#         expand("results_10M/phantomPeaks/{sample}.spp.out", sample = IPS),
+#         expand("results/phantomPeaks/{sample}.spp.pdf", sample = IPS),
+#         expand("results/phantomPeaks/{sample}.spp.Rdata", sample = IPS),
+#         expand("results/phantomPeaks/{sample}.spp.out", sample = IPS),
 #         # macs2
-#         expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
-#         expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.xls", zip, case=IPS, control=INPUTS),
-#         expand("results_10M/macs2/{case}_vs_{control}_macs2_summits.bed", zip, case=IPS, control=INPUTS),
-#         expand("results_10M/macs2/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
-#         expand("results_10M/frip/{case}_vs_{control}.frip.txt", case=IPS, control=INPUTS)
+#         expand("results/macs2/{case}_vs_{control}_macs2.HiC_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
+#         expand("results/macs2/{case}_vs_{control}_macs2.HiC_peaks.xls", zip, case=IPS, control=INPUTS),
+#         expand("results/macs2/{case}_vs_{control}_macs2.HiC_summits.bed", zip, case=IPS, control=INPUTS),
+#         expand("results/macs2/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
+#         expand("results/frip/{case}_vs_{control}.frip.txt", case=IPS, control=INPUTS)
 #     output:
-#         directory("results_10M/multiqc/multiqc_report_data/"),
-#         "results_10M/multiqc/multiqc_report.html",
+#         directory("results/multiqc/multiqc_report_data/"),
+#         "results/multiqc/multiqc_report.html",
 #     conda:
 #         "envs/multiqc.yaml"
 #     log:
-#         "results_10M/logs/multiqc.log"
+#         "results/logsHiC/multiqc.log"
 #     wrapper:
 #         "0.31.1/bio/multiqc"
diff --git a/dunnart/Snakefile_10M b/dunnart/Snakefile_10M
new file mode 100644
index 0000000000000000000000000000000000000000..093151426167286b737cc71ae60a097b9aaefb20
--- /dev/null
+++ b/dunnart/Snakefile_10M
@@ -0,0 +1,697 @@
+## Author: Laura E Cook, University of Melbourne
+## Purpose: ChIP-seq pipeline for histone marks
+## This workflow only works for paired-end sequencing reads
+    # 1. FastQC on raw reads
+    # 2. Alignment
+    # 3. Filtering
+    # 4. Alignment QC & Library Complexity
+    # 5. deepTools
+    # 6. cross correlation (SPP)
+    # 7. Call narrow peaks (MACS2)
+    # 8. Peak QC
+    # 9. Create consensus peaksets
+    # 10. Present QC for raw read, alignment, peak-calling in MultiQC
+
+#! /usr/bin/env python
+
+configfile: "envs/config.yaml"
+# Contains sample and genome information
+# calls the SRR.txt file that contains the samples as either IP or control
+
+import csv
+import os
+
+IPS = []
+INPUTS = []
+#MARK = []
+
+with open(config['SAMPLES'], "r") as f:
+    reader = csv.reader(f, delimiter = "\t")
+    # skip the header
+    header = next(reader)
+    for row in reader:
+        IPS.append(row[0])
+        INPUTS.append(row[1])
+        #MARK.append(row[2])
+
+## multiple samples may use the same control input files
+unique_inputs = list(set(INPUTS))
+#unique_marks = list(set(MARK))
+
+## combine all samples
+all_samples = IPS + unique_inputs
+
+# ===============================================================================================
+#        Output targets
+# ===============================================================================================
+
+rule all:
+    input:
+        expand("results_10M/qc/{sample}_R1_fastqc.html", sample=all_samples),
+        expand("results_10M/qc/{sample}_R2_fastqc.html", sample=all_samples),
+        expand("results_10M/qc/{sample}_R1_fastqc.zip", sample=all_samples),
+        expand("results_10M/qc/{sample}_R2_fastqc.zip", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}.sorted.bam", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}.sorted.bai", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.bam", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai", sample=all_samples),
+        "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
+        "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
+         "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam",
+        "results_10M/logs/H3K4me3.mergeBAM",
+        "results_10M/logs/H3K27ac.mergeBAM",
+        expand("results_10M/qc/{sample}.unfiltered.flagstat.qc", sample=all_samples),
+        expand("results_10M/qc/{sample}.dedup.flagstat.qc", sample=all_samples),
+        expand("results_10M/qc/{sample}.dupmark.flagstat.qc", sample=all_samples),
+        expand("results_10M/qc/{sample}.PPq30.flagstat.qc", sample=all_samples),
+        expand("results_10M/qc/{sample}.ccurve.txt", sample=all_samples),
+        expand("results_10M/qc/{sample}.extrap.txt", sample=all_samples),
+        expand("results_10M/logs/{sample}.ccurve.preseq", sample=all_samples),
+        expand("results_10M/logs/{sample}.extrap.preseq", sample=all_samples),
+        expand("results_10M/qc/{sample}_est_lib_complex_metrics.txt", sample=all_samples),
+        expand("results_10M/logs/{sample}.picardLibComplexity", sample=all_samples),
+        expand("results_10M/qc/{sample}.pbc.qc", sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.tmp.bam",sample=all_samples),
+        expand("results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam",sample=all_samples),
+        "results_10M/qc/multibamsum.npz",
+        "results_10M/qc/multibamsum.tab",
+        "results_10M/qc/pearsoncor_multibamsum.png",
+        "results_10M/qc/pearsoncor_multibamsum_matrix.txt",
+        expand("results_10M/qc/{sample}.SeqDepthNorm.bw", sample=all_samples),
+        "results_10M/qc/multiBAM_fingerprint.png",
+        "results_10M/qc/multiBAM_fingerprint_metrics.txt",
+        "results_10M/qc/multiBAM_fingerprint_rawcounts.txt",
+        "results_10M/qc/bamPEFragmentSize_hist.png",
+        "results_10M/qc/bamPEFragmentSize_rawcounts.tab",
+        expand("results_10M/bowtie2/{sample}_R1_trimmed_q30.bam", sample=all_samples),
+        expand("results_10M/logs/{sample}_filt_15Mreads.SE.spp.log", sample=all_samples),
+        expand("results_10M/qc/{sample}_filt_15Mreads.SE.cc.qc", sample=all_samples),
+        expand("results_10M/qc/{sample}_filt_15Mreads.SE.cc.plot.pdf", sample=all_samples),
+        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
+        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.xls", zip, case=IPS, control=INPUTS),
+        expand("results_10M/macs2/{case}_vs_{control}_macs2_default_summits.bed", zip, case=IPS, control=INPUTS),
+        expand("results_10M/qc/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
+        expand("results_10M/bowtie2/{case}.bedpe", case=IPS),
+        expand("results_10M/logs/{case}.bamToBed", case=IPS),
+        expand("results_10M/qc/{case}_vs_{control}.frip_default.txt", case=IPS, control=INPUTS),
+        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak",
+        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak",
+        "results_10M/macs2/H3K4me3_overlap_default.narrowPeak",
+        "results_10M/macs2/H3K27ac_overlap_default.narrowPeak",
+        "results_10M/qc/H3K4me3_overlap_default.frip",
+        "results_10M/qc/H3K27ac_overlap_default.frip"
+        #directory("results_10M/multiqc/multiqc_report_data/"),
+        # "results_10M/multiqc/multiqc_report.html"
+# ===============================================================================================
+#  1. FASTQC
+# ===============================================================================================
+
+rule fastqc:
+    input:
+        ["rawdata/{sample}_R1.fastq.gz", "rawdata/{sample}_R2.fastq.gz"]
+    output:
+        "results_10M/qc/{sample}_R1_fastqc.html",
+        "results_10M/qc/{sample}_R2_fastqc.html",
+        "results_10M/qc/{sample}_R1_fastqc.zip",
+        "results_10M/qc/{sample}_R2_fastqc.zip"
+    log:
+        "results_10M/logs/{sample}.fastqc"
+    shell:
+        "fastqc {input} -t 6 --extract --outdir=results_10M/qc/ 2> {log}"
+
+# ===============================================================================================
+#  2. ALIGNMENT
+#   > align read pairs
+# ===============================================================================================
+
+rule align:
+    input:
+        R1="rawdata/{sample}_R1.fastq.gz",
+        R2="rawdata/{sample}_R2.fastq.gz"
+    output:
+        "results_10M/bowtie2/{sample}.sorted.bam"
+    params:
+        index="genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr"
+    log:
+        "results_10M/logs/{sample}.align"
+    shell:
+        "bowtie2 --threads 8 -q -X 2000 --very-sensitive -x {params.index} -1 {input.R1} -2 {input.R2} \
+        |  samtools view -u -h  - |  samtools sort -o {output}  - 2> {log}"
+
+
+# ===============================================================================================
+#  3. FILTERING
+#   > remove unmapped, mate unmapped
+#   > remove low MAPQ reads (-q 30 (ENCODE))
+#   > keep proper pairs (-f)
+#   > mark duplicates (picard)
+#   > -F 1804
+#           -Read unmapped
+#           -Mate unmapped
+#           -Not primary alignment
+#           -Read fails platform/vendor quality checks
+#           -Read is PCR or optical duplicate
+#   > index final position sorted BAM
+# ===============================================================================================
+
+rule filter:
+    input:
+        "results_10M/bowtie2/{sample}.sorted.bam"
+    output:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.bam"
+    log:
+        "results_10M/logs/{sample}.dedup"
+    shell:
+        "samtools view -b -F 1804 -q 30 -f 2 {input} | samtools sort -o {output} - 2> {log}"
+
+rule markDups:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.bam"
+    output:
+        bam="results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam",
+        dupQC="results_10M/bowtie2/{sample}.dup.qc"
+    log:
+        "results_10M/logs/{sample}.dupmark"
+    shell:
+        "picard MarkDuplicates I={input} O={output.bam} \
+        METRICS_FILE={output.dupQC} REMOVE_DUPLICATES=FALSE ASSUME_SORTED=true 2> {log}"
+
+rule dedup:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+    output:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"
+    log:
+        "results_10M/logs/{sample}.dedup"
+    shell:
+        "samtools view -F 1804 -f 2 -b {input} | samtools sort -o {output} - 2> {log}"
+
+rule indexBam:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"
+    output:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"
+    log:
+        "results_10M/logs/{sample}.indexBam"
+    shell:
+        "samtools index -c {input} {output} 2> {log}"
+
+rule mergeBAMreplicates:
+    input:
+        H3K4me3 = ["results_10M/bowtie2/A-2_H3K4me3_PPq30.sorted.dedup.bam",  "results_10M/bowtie2/B-2_H3K4me3_PPq30.sorted.dedup.bam"],
+        H3K27ac = ["results_10M/bowtie2/A-3_H3K27ac_PPq30.sorted.dedup.bam", "results_10M/bowtie2/B-3_H3K27ac_PPq30.sorted.dedup.bam"],
+        control = ["results_10M/bowtie2/A-1_input_PPq30.sorted.dedup.bam", "results_10M/bowtie2/B-1_input_PPq30.sorted.dedup.bam"]
+    output:
+        H3K4me3 = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
+        H3K27ac = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
+        control = "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam"
+    log:
+        H3K4me3 = "results_10M/logs/H3K4me3.mergeBAM",
+        H3K27ac = "results_10M/logs/H3K27ac.mergeBAM",
+        control = "results_10M/logs/input.mergeBAM"
+    run:
+        shell("samtools merge {output.H3K4me3} {input.H3K4me3} 2> {log.H3K4me3}")
+        shell("samtools merge {output.H3K27ac} {input.H3K27ac} 2> {log.H3K27ac}")
+        shell("samtools merge {output.control} {input.control} 2> {log.control}")
+
+# ===============================================================================================
+#  4. GENERAL ALIGNMENT QC
+#   > SAMtools flagstat statistics
+#   > Compute Library Complexity (PreSeq)
+#   > picard library complexity
+#   > ENCODE library complexity
+# ===============================================================================================
+
+rule mappingStats:
+    input:
+        a="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam",
+        b="results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam",
+        c="results_10M/bowtie2/{sample}_PPq30.sorted.bam",
+        d="results_10M/bowtie2/{sample}.sorted.bam"
+    output:
+        a="results_10M/qc/{sample}.dedup.flagstat.qc",
+        b="results_10M/qc/{sample}.dupmark.flagstat.qc",
+        c="results_10M/qc/{sample}.PPq30.flagstat.qc",
+        d="results_10M/qc/{sample}.unfiltered.flagstat.qc",
+    run:
+        shell("samtools flagstat {input.a} > {output.a}")
+        shell("samtools flagstat {input.b} > {output.b}")
+        shell("samtools flagstat {input.c} > {output.c}")
+        shell("samtools flagstat {input.d} > {output.d}")
+
+
+rule downsample_bam:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+    output:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam"
+    log:
+        "results_10M/logs/{sample}.downsample"
+    shell:
+        "picard DownsampleSam I={input} O={output} P=0.35 \
+         2> {log}"
+
+rule preseq:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+    output:
+        ccurve = "results_10M/qc/{sample}.ccurve.txt",
+        extrap = "results_10M/qc/{sample}.extrap.txt"
+    log:
+        ccurve = "results_10M/logs/{sample}.ccurve.preseq",
+        extrap = "results_10M/logs/{sample}.extrap.preseq"
+    run:
+        shell("preseq lc_extrap -v -output {output.extrap} -pe -bam {input} 2> {log.extrap}")
+        shell("preseq c_curve -v -output {output.ccurve} -pe -bam {input} 2> {log.ccurve}")
+
+rule get_picard_complexity_metrics:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark.bam"
+    output:
+        "results_10M/qc/{sample}_est_lib_complex_metrics.txt"
+    log:
+        "results_10M/logs/{sample}.downSampled.picardLibComplexity"
+    shell:
+        "picard -Xmx6G EstimateLibraryComplexity INPUT={input} OUTPUT={output} USE_JDK_DEFLATER=TRUE USE_JDK_INFLATER=TRUE VERBOSITY=ERROR"
+
+rule sort_name:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.bam"
+    output:
+        tmp = "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.tmp.bam"
+    log:
+        "results_10M/logs/{sample}.pbc.sort"
+    run:
+        shell("samtools sort -n {input} -o {output.tmp} 2> {log}")
+
+rule estimate_lib_complexity:
+    input:
+        "results_10M/bowtie2/{sample}_PPq30.sorted.dupmark_downSampled.tmp.bam"
+    output:
+        qc = "results_10M/qc/{sample}.pbc.qc",
+    log:
+        "results_10M/logs/{sample}.pbc"
+    shell:
+        """
+        bedtools bamtobed -bedpe -i {input} \
+        | awk 'BEGIN{{OFS="\\t"}}{{print $1,$2,$4,$6,$9,$10}}' \
+        | sort | uniq -c \
+        | awk 'BEGIN{{mt=0;m0=0;m1=0;m2=0}} ($1==1){{m1=m1+1}} ($1==2){{m2=m2+1}} \
+        {{m0=m0+1}} {{mt=mt+$1}} END{{printf "%d\\t%d\\t%d\\t%d\\t%f\\t%f\\t%f\\n" ,mt,m0,m1,m2,m0/mt,m1/m0,m1/m2}}' \
+        > {output.qc}
+        """
+
+## convert to bedPE
+## print read 1 scaffold, read 1 start coordinate, read 2 scaffold, read 2 end coordinate, strand read 1, strand read 2
+## remove mitochondrial genome
+## sort by position and obtain uniq reads
+## Format of file
+## TotalReadPairs [tab] DistinctReadPairs [tab] OneReadPair [tab] TwoReadPairs [tab] NRF=Distinct/Total [tab] PBC1=OnePair/Distinct [tab] PBC2=OnePair/TwoPair
+
+# ===============================================================================================
+#  5. deepTools
+#   > multiBAMsummary
+#   > plotCorrelation
+#   > plotFingerprint
+#   > bamCoverage (read depth normalised bigWig files)
+#   > plotCoverage
+#   > bamPEFragmentSize
+# ===============================================================================================
+
+rule deeptools_summary:
+    input:
+        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+    output:
+        sum="results_10M/qc/multibamsum.npz",
+        counts="results_10M/qc/multibamsum.tab"
+    threads: 32
+    log:
+        "results_10M/logs/multisummary.deepTools"
+    shell:
+        " multiBamSummary bins \
+        -p {threads} \
+        -b {input.bam} \
+        --centerReads \
+        -out {output.sum} \
+        --outRawCounts {output.counts} 2> {log}"
+
+rule deeptools_correlation:
+    input: "results_10M/qc/multibamsum.npz"
+    output:
+        fig="results_10M/qc/pearsoncor_multibamsum.png",
+        matrix="results_10M/qc/pearsoncor_multibamsum_matrix.txt"
+    log:
+        "results_10M/logs/correlation.deepTools"
+    shell:
+        "plotCorrelation \
+        --corData {input} \
+        --plotFile {output.fig} \
+        --outFileCorMatrix {output.matrix} \
+        --corMethod pearson \
+        --whatToPlot heatmap \
+        --skipZeros \
+        --plotNumbers \
+        --colorMap RdYlBu 2> {log}"
+
+rule deeptools_coverage:
+    input:
+        bam ="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam",
+        bai ="results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"
+    output:
+        "results_10M/qc/{sample}.SeqDepthNorm.bw"
+    log:
+        "results_10M/logs/{sample}_coverage.deepTools"
+    shell:
+        "bamCoverage \
+        --bam {input.bam} \
+        --binSize 10 \
+        --normalizeUsing RPGC \
+        --effectiveGenomeSize 2740338543 \
+        --extendReads \
+        -o {output} 2> {log}"
+
+rule deeptools_fingerprint:
+    input:
+        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+    output:
+        fig="results_10M/qc/multiBAM_fingerprint.png",
+        metrics="results_10M/qc/multiBAM_fingerprint_metrics.txt",
+        rawcounts="results_10M/qc/multiBAM_fingerprint_rawcounts.txt"
+    threads: 32
+    log:
+        "results_10M/logs/fingerprint.deepTools"
+    shell:
+        "plotFingerprint -p {threads} \
+        -b {input.bam} \
+        --plotFile {output.fig} \
+        --outQualityMetrics {output.metrics} \
+        --outRawCounts {output.rawcounts} \
+        --minMappingQuality 30 \
+        --skipZeros \
+        --centerReads 2> {log}"
+
+rule deeptools_bamPEFragmentSize:
+    input:
+        bam = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bam"], sample=all_samples),
+        bai = expand(["results_10M/bowtie2/{sample}_PPq30.sorted.dedup.bai"], sample=all_samples)
+    output:
+        fig="results_10M/qc/bamPEFragmentSize_hist.png",
+        rawcounts="results_10M/qc/bamPEFragmentSize_rawcounts.tab"
+    log:
+        "results_10M/logs/bamPEFragmentSize.deepTools"
+    shell:
+        "bamPEFragmentSize \
+        -hist {output.fig} \
+        --outRawFragmentLengths {output.rawcounts} \
+        -b {input.bam} 2> {log}"
+
+
+# ===============================================================================================
+#  6. Cross-correlation scores - following ENCODE code
+# ===============================================================================================
+
+# Using only 1 of the read pairs, trim to 50 bp read
+# trimfastq.py is a script from the ENCODE pipeline
+
+rule trim_read1:
+    input:
+        "rawdata/{sample}_R1.fastq.gz"
+    output:
+        "results_10M/qc/{sample}_R1_trimmed.fastq.gz"
+    run:
+        shell("python scripts/trimfastq.py {input} 50 | gzip -nc > {output}")
+
+## Align trimmed read to the genome
+
+rule align_trimmed_read1:
+    input:
+        "results_10M/qc/{sample}_R1_trimmed.fastq.gz"
+    output:
+        "results_10M/bowtie2/{sample}_R1_trimmed.bam"
+    params:
+        index="genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr"
+    log:
+        "results_10M/logs/{sample}_align_trimmed_read1.log"
+    shell:
+        "bowtie2 -x {params.index} -U {input} 2> {log} | \
+        samtools view -Su - | samtools sort -o {output} - 2> {log}"
+
+## Filter alignment but don't dedup
+
+rule filter_sort_trimmed_alignment:
+    input:
+        "results_10M/bowtie2/{sample}_R1_trimmed.bam"
+    output:
+        bam = "results_10M/bowtie2/{sample}_R1_trimmed_q30.bam"
+    log:
+        "results_10M/logs/{sample}_align_trimmed_read1_filter.log"
+    run:
+        shell("samtools view -F 1804 -q 30 -b {input} -o {output.bam}")
+
+rule bamtobed_crossC:
+    input:
+        "results_10M/bowtie2/{sample}_R1_trimmed_q30.bam"
+    output:
+        tagAlign = "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
+    shell:
+        """
+        bedtools bamtobed -i {input} | \
+        awk 'BEGIN{{OFS="\\t"}}{{$4='N';$5='1000';print $0}}' |\
+        gzip -nc > {output.tagAlign}
+        """
+
+## Subsample 15 million reads for cross-correlation analysis
+## Estimate read length from first 100 reads
+rule subsample_aligned_reads:
+    input:
+        "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.gz"
+    output:
+        subsample = "results_10M/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz",
+        tmp = "results_10M/bed/{sample}_R1_trimmed_q30_SE.tagAlign.tmp"
+    params:
+        nreads= 15000000
+    run:
+        shell("""zcat {input} | shuf -n {params.nreads} --random-source=<(openssl enc -aes-256-ctr -pass pass:$(zcat -f {input} | wc -c) -nosalt </dev/zero 2>/dev/null) | gzip -nc > {output.subsample}""")
+        shell("zcat {input} > {output.tmp}")
+
+# Determine exclusion range for fragment length estimation.
+
+# Use a fixed lowerbound at -500.
+# Upperbound E
+#EXCLUSION_RANGE_MAX is
+#   Histone ChIP-seq:  max(read_len + 10, 100)
+# lowerbound is fixed at 500 for both
+
+
+rule cross_correlation_SSP:
+    input:
+        "results_10M/bed/{sample}.filt.sample.15Mreads.SE.tagAlign.gz"
+    output:
+        CC_SCORES_FILE="results_10M/qc/{sample}_filt_15Mreads.SE.cc.qc",
+        CC_PLOT_FILE="results_10M/qc/{sample}_filt_15Mreads.SE.cc.plot.pdf"
+    log:
+        "results_10M/logs/{sample}_filt_15Mreads.SE.spp.log"
+    params:
+        EXCLUSION_RANGE_MIN=-500,
+        EXCLUSION_RANGE_MAX=60
+    shell:
+        "Rscript scripts/run_spp.R -c={input} -savp={output.CC_PLOT_FILE} -out={output.CC_SCORES_FILE} -x={params.EXCLUSION_RANGE_MIN}:{params.EXCLUSION_RANGE_MAX} 2> {log}"
+
+#
+# CC_SCORE FILE format
+# Filename <tab> numReads <tab> estFragLen <tab> corr_estFragLen <tab> PhantomPeak <tab> corr_phantomPeak <tab> argmin_corr <tab> min_corr <tab> phantomPeakCoef <tab> relPhantomPeakCoef <tab> QualityTag
+
+
+# ===============================================================================================
+#  7. Call peaks (MACS2)
+# ===============================================================================================
+
+# peak calling for pair-end peaks
+# effective genome size for dunnart genome calculated with khmer program (see README.md)
+
+rule call_peaks_macs2:
+    input:
+        control = "results_10M/bowtie2/{control}_PPq30.sorted.dedup.bam",
+        case = "results_10M/bowtie2/{case}_PPq30.sorted.dedup.bam"
+    output:
+        "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.xls",
+        "results_10M/macs2/{case}_vs_{control}_macs2_default_summits.bed",
+        "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak",
+    log:
+        "results_10M/logs/{case}_vs_{control}_call_peaks_macs2_default.log"
+    params:
+        name = "{case}_vs_{control}_macs2_default",
+    shell:
+        " macs2 callpeak -f BAMPE -t {input.case} \
+        -c {input.control} --keep-dup all \
+        --outdir results_10M/macs2/  \
+        -n {params.name} \
+        -g 2740338543 2> {log} "
+
+rule call_peaks_macs2_pooled_replicates:
+    input:
+        H3K4me3 = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
+        H3K27ac = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
+        input = "results_10M/bowtie2/input_pooled_PPq30.sorted.dedup.bam"
+    output:
+        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.xls",
+        "results_10M/macs2/H3K4me3_pooled_macs2_default_summits.bed",
+        "results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak",
+        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.xls",
+        "results_10M/macs2/H3K27ac_pooled_macs2_default_summits.bed",
+        "results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak"
+    log:
+        H3K4me3 ="results_10M/logs/H3K4me3_pooled_call_peaks_macs2_default.log",
+        H3K27ac ="results_10M/logs/H3K27ac_pooled_call_peaks_macs2_default.log"
+    params:
+        H3K4me3 = "H3K4me3_pooled_macs2_default",
+        H3K27ac = "H3K27ac_pooled_macs2_default"
+    run:
+        shell(" macs2 callpeak -f BAMPE -t {input.H3K4me3} \
+        -c {input.input} --keep-dup all \
+        --outdir results_10M/macs2/ \
+        -n {params.H3K4me3} \
+        -g 2740338543 2> {log.H3K4me3} ")
+        shell("macs2 callpeak -f BAMPE -t {input.H3K27ac} \
+        -c {input.input} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.H3K27ac} \
+        -g 2740338543 2> {log.H3K27ac} ")
+
+# ===============================================================================================
+#  8. Peak QC
+#   > narrow peak counts
+#   > fraction of reads in peaks (convert BAM to bed first then do intersect with peaks)
+# ===============================================================================================
+
+# peak counts in a format that multiqc can handle
+rule get_narrow_peak_counts_for_multiqc:
+    input:
+        peaks = "results_10M/macs2/{case}_vs_{control}_macs2_peaks.narrowPeak"
+    output:
+        "results_10M/qc/{case}-vs-{control}-narrowpeak-count_mqc.json"
+    params:
+        peakType = "narrowPeak"
+    shell:
+        "python3 scripts/count_peaks.py \
+        --peak_type {params.peakType} \
+        --peaks {input.peaks} --sample_name {wildcards.case} > {output}"
+
+
+## Convert BAM to tagAlign file for calculating FRiP QC metric (Fraction of reads in peaks)
+rule bamToBed:
+    input:
+        "results_10M/bowtie2/{case}_PPq30.sorted.dedup.bam"
+    output:
+        "results_10M/bowtie2/{case}.bedpe"
+    log:
+        "results_10M/logs/{case}.bamToBed"
+    shell:
+        "samtools sort -n {input} | bedtools bamtobed -bedpe -mate1 -i - > {output}"
+
+
+## Fraction of reads in peaks
+rule frip:
+    input:
+        bed = "results_10M/bowtie2/{case}.bedpe",
+        peak = "results_10M/macs2/{case}_vs_{control}_macs2_default_peaks.narrowPeak"
+    output:
+        "results_10M/qc/{case}_vs_{control}.frip_default.txt"
+    shell:
+        "python2.7 scripts/encode_frip.py {input.bed} {input.peak} > {output}"
+
+
+# ===============================================================================================
+#  9. Create consensus peaksets for replicates
+# ===============================================================================================
+
+# Based on ENCODE `overlap_peaks.py` - recommended for histone marks.
+# Need to pool peaks first for each replicate
+
+rule overlap_peaks_H3K4me3:
+    input:
+        peak1="results_10M/macs2/A-2_H3K4me3_vs_A-1_input_macs2_default_peaks.narrowPeak",
+        peak2="results_10M/macs2/B-2_H3K4me3_vs_B-1_input_macs2_default_peaks.narrowPeak",
+        pooled="results_10M/macs2/H3K4me3_pooled_macs2_default_peaks.narrowPeak"
+    output:
+        "results_10M/macs2/H3K4me3_overlap_default.narrowPeak"
+    shell:
+        "python2.7 scripts/overlap_peaks.py {input.peak1} {input.peak2} {input.pooled} {output}"
+
+
+rule overlap_peaks_H3K27ac:
+    input:
+        peak1="results_10M/macs2/A-3_H3K27ac_vs_A-1_input_macs2_default_peaks.narrowPeak",
+        peak2="results_10M/macs2/B-3_H3K27ac_vs_B-1_input_macs2_default_peaks.narrowPeak",
+        pooled="results_10M/macs2/H3K27ac_pooled_macs2_default_peaks.narrowPeak"
+    output:
+        "results_10M/macs2/H3K27ac_overlap_default.narrowPeak"
+    shell:
+        "python2.7 scripts/overlap_peaks.py {input.peak1} {input.peak2} {input.pooled} {output}"
+
+## Fraction of reads in peaks
+rule overlap_frip:
+    input:
+        H3K4me3bam = "results_10M/bowtie2/H3K4me3_pooled_PPq30.sorted.dedup.bam",
+        H3K27acbam = "results_10M/bowtie2/H3K27ac_pooled_PPq30.sorted.dedup.bam",
+        H3K4me3bed = "results_10M/macs2/H3K4me3_overlap_default.narrowPeak",
+        H3K27acbed = "results_10M/macs2/H3K27ac_overlap_default.narrowPeak"
+    output:
+        H3K4me3frip = "results_10M/qc/H3K4me3_overlap_default.frip",
+        H3K27acfrip = "results_10M/qc/H3K27ac_overlap_default.frip"
+    run:
+        shell("python2.7 scripts/encode_frip.py {input.H3K4me3bam} {input.H3K4me3bed} > {output.H3K4me3frip}")
+        shell("python2.7 scripts/encode_frip.py {input.H3K27acbam} {input.H3K27acbed} > {output.H3K27acfrip}")
+
+#  10. Combine all QC into multiqc output
+# ===============================================================================================
+#
+# rule multiqc:
+#     input:
+#         # fastqc
+#         expand("results_10M/qc/{sample}_R1_fastqc.html", sample=all_samples),
+#         expand("results_10M/qc/{sample}_R2_fastqc.html", sample=all_samples),
+#         expand("results_10M/qc/{sample}_R1_fastqc.zip", sample=all_samples),
+#         expand("results_10M/qc/{sample}_R2_fastqc.zip", sample=all_samples),
+#         # bowtie2
+#         expand("results_10M/logs/{sample}.align", sample=all_samples),
+#         expand("results_10M/qc/{sample}.flagstat.qc", sample=all_samples),
+#         # preseq
+#         expand("results_10M/qc/{sample}.ccurve.txt", sample=all_samples),
+#         expand("results_10M/qc/{sample}.extrap.txt", sample=all_samples),
+#         # deepTools
+#         "results_10M/deeptools/multibamsum.npz",
+#         "results_10M/deeptools/multibamsum.tab",
+#         "results_10M/deeptools/pearsoncor_multibamsum.png",
+#         "results_10M/deeptools/pearsoncor_multibamsum_matrix.txt",
+#         expand("results_10M/deeptools/{sample}.SeqDepthNorm.bw", sample=all_samples),
+#         "results_10M/deeptools/multiBAM_fingerprint.png",
+#         "results_10M/deeptools/multiBAM_fingerprint_metrics.txt",
+#         "results_10M/deeptools/multiBAM_fingerprint_rawcounts.txt",
+#         "results_10M/deeptools/plot_coverage.png",
+#         "results_10M/deeptools/plot_coverage_rawcounts.tab",
+#         "results_10M/deeptools/bamPEFragmentSize_hist.png",
+#         "results_10M/deeptools/bamPEFragmentSize_rawcounts.tab",
+#         # phantomPeaks
+#         expand("results_10M/phantomPeaks/{sample}.spp.pdf", sample = IPS),
+#         expand("results_10M/phantomPeaks/{sample}.spp.Rdata", sample = IPS),
+#         expand("results_10M/phantomPeaks/{sample}.spp.out", sample = IPS),
+#         # macs2
+#         expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
+#         expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.xls", zip, case=IPS, control=INPUTS),
+#         expand("results_10M/macs2/{case}_vs_{control}_macs2_summits.bed", zip, case=IPS, control=INPUTS),
+#         expand("results_10M/macs2/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
+#         expand("results_10M/frip/{case}_vs_{control}.frip.txt", case=IPS, control=INPUTS)
+#     output:
+#         directory("results_10M/multiqc/multiqc_report_data/"),
+#         "results_10M/multiqc/multiqc_report.html",
+#     conda:
+#         "envs/multiqc.yaml"
+#     log:
+#         "results_10M/logs/multiqc.log"
+#     wrapper:
+#         "0.31.1/bio/multiqc"
diff --git a/dunnart/configs/.multiqc_config.yaml b/dunnart/configs/.multiqc_config.yaml
old mode 100644
new mode 100755
diff --git a/dunnart/configs/SRR.txt b/dunnart/configs/SRR.txt
old mode 100644
new mode 100755
diff --git a/dunnart/configs/cluster.json b/dunnart/configs/cluster.json
old mode 100644
new mode 100755
diff --git a/dunnart/configs/config.yaml b/dunnart/configs/config.yaml
old mode 100644
new mode 100755
diff --git a/dunnart/scripts/.DS_Store b/dunnart/scripts/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
Binary files /dev/null and b/dunnart/scripts/.DS_Store differ
diff --git a/dunnart/scripts/GC_CpG_plots.R b/dunnart/scripts/GC_CpG_plots.R
new file mode 100644
index 0000000000000000000000000000000000000000..418a54f6c50626597395888d8fbce0756810664c
--- /dev/null
+++ b/dunnart/scripts/GC_CpG_plots.R
@@ -0,0 +1,77 @@
+# GC content and CpG % plots 
+
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(dplyr)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/")
+promoter <- fread("annotations/dunnart_promoters_homerAnnot.txt")
+enhancer <- fread("annotations/dunnart_enhancers_homerAnnot.txt")
+peaks = list(promoter, enhancer)
+peaks = Map(mutate,peaks,cre=c("promoters", "enhancers"))
+peaks = lapply(peaks, function(x) x %>% dplyr::select("Chr", "Start", "End", "Peak Score", "Distance to TSS", "CpG%", "GC%", "cre") %>% as.data.table())
+peaks = rbindlist(peaks)
+colnames(peaks)[5:7] <- c("distanceToTSS","CpG", "GC")
+peaks[is.na(peaks)] <- 0
+peaks = peaks[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+promoter = subset(peaks, cre == "promoters")
+enhancer = subset(peaks, cre == "enhancers")
+promoter_k = copy(promoter)
+
+## kmeans clustering
+promoter.kmeans = kmeans(promoter_k[,9], 3, iter.max = 1000, nstart=25)
+promoter.kmeans_table = data.frame(promoter.kmeans$size, promoter.kmeans$centers)
+promoter.kmeans_df = data.frame(Cluster = promoter.kmeans$cluster, promoter_k[,9])
+promoter_k$meanK <- promoter.kmeans_df$Cluster
+enhancer$meanK <- "enhancer"
+promoter$meanK <- "promoter"
+
+p <- ggplot(promoter.kmeans_df, aes(x=log10_abs_dist, fill=as.factor(Cluster), color=as.factor(Cluster))) + 
+            geom_histogram(binwidth=0.1, position = 'identity')  +
+            theme_bw()+ font
+  pdf("homer.Annot.kmeans.pdf", width = 10, height = 7)
+  print(p + scale_color_manual(values = c('#4D004B','#8C6BB1','#9EBCDA')) + scale_fill_manual(values = c('#4D004B','#8C6BB1','#9EBCDA')))
+  dev.off()
+
+data = rbind(enhancer, promoter, promoter_k)
+
+p <- ggplot(data, aes(factor(meanK), y = GC)) + 
+  geom_violin(aes(fill=factor(meanK), color=factor(meanK)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(meanK)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#D3BFD2","#D4C7E2","#E7EEF6", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("GC content") 
+  pdf("dunnart_GC.pdf", width=9, height = 8)
+  print(p +  scale_color_manual(values = c('#4D004B','#8C6BB1','#9EBCDA', '#E9C46A','#2A9D8F')) +
+  scale_fill_manual(values = c("#7A4078","#A990C4","#B6CDE3" ,"#F1DAA2","#7AC2B9")) + font) 
+  dev.off()
+
+  p <- ggplot(data, aes(factor(meanK), y = CpG)) + 
+  geom_violin(aes(fill=factor(meanK), color=factor(meanK)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(meanK)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#D3BFD2","#D4C7E2","#E7EEF6", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("CpG") 
+  pdf("dunnart_CpG.pdf", width=9, height = 8)
+  print(p +  scale_color_manual(values = c('#4D004B','#8C6BB1','#9EBCDA', '#E9C46A','#2A9D8F')) +
+  scale_fill_manual(values = c("#7A4078","#A990C4","#B6CDE3" ,"#F1DAA2","#7AC2B9")) + font) 
+  dev.off()
\ No newline at end of file
diff --git a/dunnart/scripts/GO_plots.R b/dunnart/scripts/GO_plots.R
new file mode 100755
index 0000000000000000000000000000000000000000..8792515b5c0b7bbb1fb44220cd7d09ed302ee9a0
--- /dev/null
+++ b/dunnart/scripts/GO_plots.R
@@ -0,0 +1,80 @@
+## Script that takes as input file with GO enriched terms and returns you a barplot
+library(dplyr); library(data.table)
+library(magrittr); 
+library(ggthemes);library(ggplot2);library(ggpubr)
+
+## change here the working directory(ies)
+
+go_files_dir='/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/annotations/KEGG/'
+plot_dir='/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/plots/'
+
+## read files into the directory
+## it creates a list of dataframes (one per file)
+## then discards the geneID field 
+
+read_files=function(dir){
+  x=as.character(list.files(dir,recursive =F,full.names = T)) %>% 
+    lapply(function(y)
+      fread(y,sep = '\t',header = T)[
+        ,geneID:=NULL
+      ] %>% setorderv('p.adjust',1)) ## sorted by most significant ones 
+  ## this returns you the filename without file extension. 
+  ## I will use it as plot title below (you can remove it if u want)
+  file_names=as.character(list.files(dir,recursive =F,full.names = F))
+  file_names=gsub("\\..*","",file_names) 
+  names(x)=file_names
+  ## I will make a new field with the filename that I will use as categorical variable to assign different colors
+  ## i.e. each plot will have a different color
+  x=Map(mutate,x,filename=names(x))
+  x=lapply(x,function(y)y=as.data.table(y))
+  return(x)
+}
+go_terms=read_files(go_files_dir)
+
+## I will make another column with the color for the given file. 
+## all row entries will be identical but I just need this for plotting
+## I am going to do in this way so that u can change the colors as u like for each file
+
+colors=c('#6699CC','#6699CC','#6699CC','#6699CC') ## remember to add a color per input file
+
+go_colors=Map(cbind, go_terms, color=colors)
+go_colors=lapply(go_colors, function(x)x=as.data.table(x))
+
+## this keeps only the significant GO terms
+significant_go_terms=lapply(go_colors,function(x)x=x[p.adjust<=0.05])
+
+plot_results=function(file){
+  
+  top_n_GOs=copy(file)
+  top_n_GOs=top_n_GOs[1:30]
+  
+  ggplot(top_n_GOs, 
+         aes(x=reorder(Description,-log10(p.adjust)), y=-log10(p.adjust),fill=filename)) + ## this reorders the x-axis and converts the p-adj to log10 values
+    geom_bar(stat = 'identity',position = 'dodge')+
+    xlab(" ") +
+    ylab("\n -Log10 (P) \n ") +
+    scale_y_continuous(breaks = round(seq(0, max(-log10(top_n_GOs$p.adjust)), by = 2), 1)) +
+    scale_fill_manual(name= " ",values=top_n_GOs$color)+
+    theme( ## these are just parameters to change the theme of the plot. you can add more or remove them
+      legend.position='none',
+      legend.background=element_rect(),
+      axis.text.x=element_text(angle=0, hjust=1.10),
+      axis.text.y=element_text(angle=0, vjust=0.8),
+      axis.title=element_text(),
+      axis.line = element_line(color = "black",size = 0, linetype = "solid"),
+      panel.background =element_rect(fill = 'white', size = 0.5,colour = 'black'),
+      panel.grid.minor = element_blank(),
+      panel.grid.major = element_blank(),
+      title=element_text()) +
+    coord_flip()
+  
+}
+
+go_plot=lapply(significant_go_terms,function(x)x=plot_results(x))
+
+
+# Save each plot to a different pdf in the plot_dir/
+lapply(names(go_plot), 
+       function(x)ggsave(filename=paste(plot_dir,x,'_plot',".pdf",sep=""), 
+                         plot=go_plot[[x]]))
+
diff --git a/dunnart/scripts/array_commands.sh b/dunnart/scripts/array_commands.sh
new file mode 100755
index 0000000000000000000000000000000000000000..df4f791e2727427157abb159ddf694d5e7668753
--- /dev/null
+++ b/dunnart/scripts/array_commands.sh
@@ -0,0 +1,12 @@
+lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] 
+/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00001_pi
+lon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipelin
+e/cross_species/data/genomes/maf/scaffold00001_pilon_pilon_mm10.smiCra1.maf
+lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] 
+/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00003_pi
+lon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipelin
+e/cross_species/data/genomes/maf/scaffold00003_pilon_pilon_mm10.smiCra1.maf
+lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] 
+/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00004_pi
+lon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipelin
+e/cross_species/data/genomes/maf/scaffold00004_pilon_pilon_mm10.smiCra1.maf
\ No newline at end of file
diff --git a/cross_species_comparison/scripts/array_wrapper.slurm b/dunnart/scripts/array_wrapper.slurm.sh
old mode 100644
new mode 100755
similarity index 78%
rename from cross_species_comparison/scripts/array_wrapper.slurm
rename to dunnart/scripts/array_wrapper.slurm.sh
index e350115f9832be8e131d35ef855ae936b7a7b228..db30988b24f075424a0aec09a3e52626e0783f52
--- a/cross_species_comparison/scripts/array_wrapper.slurm
+++ b/dunnart/scripts/array_wrapper.slurm.sh
@@ -5,7 +5,7 @@
 ### Purpose: basic array wrapper script
 
 # Array set up:
-#SBATCH --array=618-718
+#SBATCH --array=1-10
 
 # Partition for the job:
 
@@ -26,23 +26,26 @@
 #SBATCH --mail-user=lecook@student.unimelb.edu.au
 
 # The maximum running time of the job in days-hours:mins:sec
-#SBATCH --time=50:00:00
+#SBATCH --time=100:00:00
 
 # The name of the job:
 #SBATCH --job-name=array
 
 # Output control:
-#SBATCH --error="/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/logs/lastz_%a.stderr"
-#SBATCH --output="/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/logs/lastz_%a.stdout"
+#SBATCH --error="/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/logs/mafToAxt_%a.stderr"
+#SBATCH --output="/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/logs/mafToAxt_%a.stdout"
 
 # Load modules:
 
 module load foss
 module load lastz
-conda activate wga
+#conda activate wga
+module load last/1066
+module load python/2.7.16
+module load ucsc/21072020
 
 # Run the simulations:
-command=`head -n $SLURM_ARRAY_TASK_ID /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/scripts/lastz_commands.sh | tail -n 1`
+command=`head -n $SLURM_ARRAY_TASK_ID /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/scripts/array_commands.sh | tail -n 1`
 echo "SLURM_ARRAY_TASK_ID: " $SLURM_ARRAY_TASK_ID
 echo $command
 eval $command
\ No newline at end of file
diff --git a/dunnart/scripts/convergentTFBS_enrichment.R b/dunnart/scripts/convergentTFBS_enrichment.R
new file mode 100755
index 0000000000000000000000000000000000000000..d5633b5adc93cc498297e4d0722bd72f508c2aee
--- /dev/null
+++ b/dunnart/scripts/convergentTFBS_enrichment.R
@@ -0,0 +1,168 @@
+
+## Author: Davide Vespasiani 2020
+
+library(data.table);library(magrittr);library(dplyr)
+library(tidyr);library(openxlsx) ## this is for exporting tables in excel format
+library(wesanderson);library(RColorBrewer); library(viridis);library(viridisLite)## these  are just for colors
+library(ggthemes);library(ggplot2);library(ggpubr)
+library(ggrepel)
+library(readxl)
+
+
+numb_threads=getDTthreads()
+threads=setDTthreads(numb_threads-1)
+
+setwd('/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/5-TFBS/4-Results/1-BioMotif/')
+
+plot_dir='~/Desktop' 
+table_dir='~/Desktop/'
+
+input_file <- fread("motifs2.csv", sep=",", header=TRUE)
+
+## these lines count the number of twars per condition (i.e. lost/gained/conserved) in each cluster
+## this is done with data.table package (if you dont know and you'll need R my adise is to look for it, it's very nice)
+## in any case when you see object=object[,colname:=something] it basically creates a column (colname) with entries corresponding on the arguments on the right
+## or if you see object=object[,c('colname1,'colname2'...)], it subset the objects retaining only the columns specified
+input_file_counts=copy(input_file)
+input_file_counts=input_file_counts[,c('twar','gain.lost','vierstra_cluster_no.','vierstra_cluster_name')] %>% 
+  unique() ## ps: if you have a twar that disrupts the motif recognised by >1 tf belonging to the same cluster calling unique here will remove this duplicate and count it only once 
+input_file_counts=input_file_counts[,tfs_in_cluster_percondition:=.N,by=.(vierstra_cluster_no.,gain.lost)][ ## this means count all rows (i.e. .N) by= factor level in specified column
+  ,totTFs_percondition:=.N,by=.(gain.lost) 
+  ][,tfs_not_cluster_percondition:=totTFs_percondition-tfs_in_cluster_percondition] ## row-wise subtraction
+
+## this function returns you a simplified version of the df subsetting it for the specified condition
+simplify_table=function(x,condition){
+  df=copy(x)[gain.lost%in%condition][
+    ,c('vierstra_cluster_no.','vierstra_cluster_name','tfs_in_cluster_percondition','tfs_not_cluster_percondition')
+    ] %>% unique()## this unique here simplifies the resulting table
+}
+
+input_file_counts_altered=simplify_table(input_file_counts,'Altered')
+input_file_counts_lost=simplify_table(input_file_counts,'Lost')
+input_file_counts_conserved=simplify_table(input_file_counts,'Conserved')
+
+concatenate_df=function(x){
+  bgkr=input_file_counts_conserved
+  test=copy(x)
+  ## this line is an inner join, so it returns all rows of test where there is a matching value in bkgr for rows of the two columns specified in 'on' argument
+  ## i.e. it returns the clusters in the test set that are also in the bkgr set so that u can create the matrix for fisher test
+  test=test[bgkr,on=c('vierstra_cluster_no.','vierstra_cluster_name'),nomatch=0]%>% 
+    setnames(old=c('tfs_in_cluster_percondition','tfs_not_cluster_percondition','i.tfs_in_cluster_percondition','i.tfs_not_cluster_percondition'),
+             new=c('test_in_cluster','test_not_cluster','bkgr_in_cluster','bkgr_not_cluster'))
+}
+
+input_altered_vs_conserved=concatenate_df(input_file_counts_altered)
+
+input_gained_vs_conserved=concatenate_df(input_file_counts_gained)
+input_lost_vs_conserved=concatenate_df(input_file_counts_lost)
+
+## this function computes the fisher.exact pvalues and fdr corrects them
+fisher_pvalues=function(x){
+  pvals=copy(x)
+  pvals=pvals[,c('vierstra_cluster_no.','vierstra_cluster_name'):=NULL]
+  pvals=apply(pvals, 1, 
+              function(y) {
+                tbl <- matrix(as.numeric(y), ncol=2, byrow=T)
+                fisher.test(tbl)$p.value
+              }) ## this function applies to every row in your df. for each row it creates the 2x2 matrix and perform fisher test and returns just the p.value as vector of characters
+  ## ps if you are not sure how it works/dont believe me (i would understand it) just run this:
+  ## pvals=copy(your_input); pvals=pvals[,c(1:2):=NULL][1,] take first row only and then matrix(as.numeric(pvals), ncol=2, byrow=T). you'll see how it makes the 2x2 table
+  
+  pvalues_table=data.table(pval=pvals) ## then this converts the list of pvalues into a column (orders are maintained, so the first pval refers to the fisrt row in the input dataframe and so on)
+  pvalues_table=pvalues_table[
+    ,adj_p:=p.adjust(pval,method = 'fdr')][ ## this creates another column with the adj_pvalues 
+      ,log10_p_adjust:=-log10(adj_p)
+      ][ 
+        ,significant_score:=ifelse(`adj_p`<=0.0001,'****',
+                                   ifelse(`adj_p`>0.0001 &`adj_p`<=0.001,'***',
+                                          ifelse(`adj_p`>0.001 & `adj_p`<=0.01,'**',
+                                                 ifelse(`adj_p`>0.01 & `adj_p`<=0.05,'*',' '))))
+        ] ## this is just for quick visualisation/subsecting of only the significant ones  
+  
+  return(pvalues_table)
+}
+
+input_altered_vs_conserved_pvals=fisher_pvalues(input_altered_vs_conserved)
+
+input_gained_vs_conserved_pvals=fisher_pvalues(input_gained_vs_conserved)
+input_lost_vs_conserved_pvals=fisher_pvalues(input_lost_vs_conserved)
+
+## now it creates final df with all columns you need 
+altered_vs_conserved=cbind(input_altered_vs_conserved,input_altered_vs_conserved_pvals)
+
+gained_vs_conserved=cbind(input_gained_vs_conserved,input_gained_vs_conserved_pvals)
+lost_vs_conserved=cbind(input_lost_vs_conserved,input_lost_vs_conserved_pvals)
+
+## this computes fold enrichment of the number of twars lost/gained for each cluster over that of conserved ones for the same cluster
+## ps this is the way i have been calculating the fold enrichmnent(i.e. the ratio of your test set/bkgr set in the cluster over the mean of this ratio across all clusters) but you could change it 
+fold_enrichment=function(x,group){
+  df=copy(x)
+  df=df[
+    ,group:=group  ## ps: i have created here a column that takes values specified in group argument (i.e. gained/lost) because this allows me at the end to combine the 2 df and use a single function to plot them together (in separate facets) -see below
+    ][,ratio_twar_lostgain_cluster:=test_in_cluster/bkgr_in_cluster
+      ][,mean_ratio:=mean(ratio_twar_lostgain_cluster)
+        ][,log2_fold_enrichment:=log2(ratio_twar_lostgain_cluster/mean_ratio)
+          ][,log10_numb_twars_incluster:=log10(test_in_cluster)
+            ][,c('vierstra_cluster_no.','vierstra_cluster_name','log2_fold_enrichment','pval','adj_p',
+                 'significant_score','log10_p_adjust','log10_numb_twars_incluster','group')
+              ]%>% setorderv('log10_p_adjust',-1)
+}
+
+altered_vs_conserved_final=fold_enrichment(altered_vs_conserved,'Altered')
+
+gained_vs_conserved_final=fold_enrichment(gained_vs_conserved,'Gained')
+lost_vs_conserved_final=fold_enrichment(lost_vs_conserved,'Lost')
+
+## here i make a single dt
+final_table=rbind(gained_vs_conserved_final,lost_vs_conserved_final)
+
+
+final_table=rbind(lost_vs_conserved_final)
+
+
+## make volcano plot
+tf_enrich_plot=function(x){
+  df=copy(x)[,'Log10 total TFs per Cluster':=log10_numb_twars_incluster]
+  gradient=scale_colour_viridis(aes(`Log10 total TFs per cluster`),option="inferno",discrete = F)
+  #text=ifelse(!df$significant_score%in%' ',df$vierstra_cluster_name,'') ## this wont report in the plot the name of the clusters that are not significantly enriched/depleted
+  
+  ggplot(df,aes(x=log2_fold_enrichment,log10_p_adjust,label = text, col=log10_numb_twars_incluster))+
+    geom_point(size=2)+
+    geom_vline(xintercept=0, linetype="dashed", color = "black",size=0.2)+
+    geom_text_repel(size = 5,color='black',
+                    box.padding = unit(0.5, "lines"),
+                    point.padding = unit(0.5, "lines")
+    )+
+    gradient+
+    xlab('\n Log2 fold enrichment \n')+
+    ylab('-Log10 (P)')+
+    xlim(-4,4)+
+    facet_wrap(group~.,ncol = 2)+ ## so this is what i was saying above: the group column allows now to split the plots into 2 facets 
+    theme(strip.text.x = element_text(),
+          strip.text.y = element_text(hjust = 0.5),
+          strip.background = element_rect(color = 'black', linetype = 'solid'),
+          strip.background.y = element_blank(),
+          strip.background.x =element_blank(),
+          panel.spacing=unit(1, "lines"),
+          panel.background =element_rect(fill = 'white', colour = 'black',size=1),
+          panel.grid.minor = element_blank(),
+          panel.grid.major = element_blank(),
+          legend.position = "bottom",
+          legend.key = element_rect(fill = "white", colour = "black"),
+          axis.line = element_blank())
+}
+
+
+pdf(paste(plot_dir,'TFBS_enrichment_fisher_test_volcano.pdf',sep=''),width = 10,height=6)
+tf_enrich_plot(final_table)
+dev.off()
+
+## This code below produces an excel file with gained/lost enrichment results in two ≠ sheets 
+
+excel_table=copy(final_table)
+excel_table=split(excel_table,as.factor(excel_table$group)) %>%lapply(function(y)y=y[
+  ,c('group','vierstra_cluster_no.','vierstra_cluster_name','log2_fold_enrichment','log10_numb_twars_incluster','pval','adj_p','log10_p_adjust','significant_score')]%>%
+    unique())
+names(excel_table)=c('altered') ## name of the sheets
+
+write.xlsx(excel_table,paste(table_dir,'Table_twars_fisher_pvalues.xlsx',sep=''))
\ No newline at end of file
diff --git a/dunnart/scripts/count_peaks.py b/dunnart/scripts/count_peaks.py
old mode 100644
new mode 100755
diff --git a/dunnart/scripts/create_jobs.sh b/dunnart/scripts/create_jobs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..9329754edea39e4e3df6bbced86ae42c102aa6d5
--- /dev/null
+++ b/dunnart/scripts/create_jobs.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+## This script loops through all files in a directory
+## These commands are then used in a slurm array script to run jobs in parallel
+
+TRA=($(for file in *.maf; do echo $file |cut -d "." -f 1-2;done)) # change this to whatever your file prefix is
+
+echo ${TRA[@]}
+
+for tr in ${TRA[@]};
+
+do
+
+# generate lastz commands
+echo 'lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/'${tr}.fa 'H=2000 K=2400 L=3000 Y=3400 --scores=/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/GenomeAlignmentTools/HoxD55.q --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/maf/'${tr}_mm10.smiCra1.maf
+
+
+# generate convert maf to axt format commands
+echo 'maf-convert psl '${tr}.maf' > '${tr}.psl
+
+done
\ No newline at end of file
diff --git a/dunnart/scripts/encode_frip.py b/dunnart/scripts/encode_frip.py
old mode 100644
new mode 100755
diff --git a/dunnart/scripts/genes.peaks_perScaffold.R b/dunnart/scripts/genes.peaks_perScaffold.R
new file mode 100644
index 0000000000000000000000000000000000000000..79d64b3c1d630a1b33bba7c127ca857460957d1d
--- /dev/null
+++ b/dunnart/scripts/genes.peaks_perScaffold.R
@@ -0,0 +1,100 @@
+## Number of peaks per gene and distance to the nearest gene
+library(dplyr)
+library(tidyverse)
+library(data.table)
+library(rtracklayer)
+library(ggpubr)
+library(grid)
+
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/annotations/")
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/")
+
+getGeneCoord <- function(fileList, promoter, enhancer){
+  files = as.list(fileList)
+  data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  subset = lapply(data, function(x) x %>% dplyr::select(seqnames, geneId, geneStart, geneEnd, geneStrand) %>% as.data.table())
+  reorder = lapply(subset, function(x) setcolorder(x, c("seqnames", "geneStart", "geneEnd", "geneId", "geneStrand")) %>% as.data.table())
+  reorder = lapply(reorder, function(x) x[,geneStrand := as.character(geneStrand)][geneStrand == "1", geneStrand := "+"])
+  reorder = lapply(reorder, function(x) x[,geneStrand := as.character(geneStrand)][geneStrand == "2", geneStrand := "-"])
+  write.table(reorder[[1]], enhancer, sep="\t", quote=F, row.names=F, col.names=F)
+  write.table(reorder[[2]], promoter, sep="\t", quote=F, row.names=F, col.names=F)
+}
+getGeneCoord(fileList = c("dunnart_enhancer_annotation.txt", "dunnart_promoter_annotation.txt"), enhancer ="enhancer_reordered_geneCoords.bed", promoter="promoter_reordered_geneCoords.bed")
+
+#system("bedtools sort -i enhancer_reordered_geneCoords.bed | bedtools merge -i stdin -s -c 5,5,6 -o count,distinct,distinct > enhancer_reordered_geneCoordsMerged.bed", intern = TRUE) ## sort by coordinates
+#system("bedtools sort -i promoter_reordered_geneCoords.bed | bedtools merge -i stdin -s -c 5,5,6 -o count,distinct,distinct > promoter_reordered_geneCoordsMerged.bed", intern = TRUE) ## bedtools merge where transcripts are the same coordinates or within each other
+
+genesPerScaffold <- function(file, distances){
+  data = fread(file, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")) # read in all files
+  colnames(data) <- c("seqnames", "start", "end","gene", "strand")
+  data$type <- "peak"
+  peak.counts <- data %>% as.data.frame() %>% group_by(gene) %>%
+    summarise(count = n()) %>% as.data.table()
+
+  gffRangedData<-import.gff("../Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff")
+  allGenes<-as(gffRangedData, "GRanges")
+  allGenesDT <- as.data.table(allGenes)
+  allGenesDT = allGenesDT %>% dplyr::select(seqnames, start, end, strand, type, gene) %>% as.data.table() %>% setcolorder(c("seqnames", "start", "end","gene", "strand", "type"))
+  allGenesDT.filtered = dplyr::filter(allGenesDT, type == "gene")
+  allGenesDT.filtered = setcolorder(allGenesDT.filtered, c("seqnames", "start", "end","gene", "strand", "type"))
+  bind = rbind(data, allGenesDT.filtered)
+  split = bind %>% split(by="seqnames") ## split by scaffold
+ # df = lapply(split, function(x) rbindlist(x)) ## bind rows back together
+  split = lapply(split, function(x) subset(x, nrow(x) > 1)) ## remove dataframes that only have 1 row per gene
+  split = split[sapply(split, function(x) dim(x)[1]) > 0] ## remove empty dataframes
+
+  ## Count number of genes per scaffold and number of peaks
+  scaffold.vs.peakCount = copy(split)
+  metrics = lapply(scaffold.vs.peakCount, function(x) as.data.frame(x) %>% group_by(type) %>%
+    summarise(count = n(), .groups = 'drop') %>% as.data.table())
+  split2 = lapply(metrics, function(x) subset(x, nrow(x) > 1)) ## remove dataframes that only have 1 row per gene
+  filter = split2[sapply(split2, function(x) dim(x)[1]) > 0] ## remove empty dataframes
+  df1 = Map(mutate, filter, scaffold = names(filter))
+  df1 = lapply(df1, function(x) x = dcast(x, scaffold ~ type, value.var = "count"))
+  df1 = rbindlist(df1,) ## bind rows back together
+  return(df1)
+
+  ## Now compare the distances between the nearest gene and number of peaks
+  split = lapply(split, function(x) unique(x, by = "gene"))
+  split = lapply(split, function(x) x=setorderv(x,c('start', 'end'),1)) ## order peaks on start stop coordinates
+  split = lapply(split, function(x) x=x[,distanceBetweenPeaks:=lead(start)-start]) ## find distance between peaks with the same nearest gene
+  split = lapply(split, function(x) x=x[,distanceBetweenPeaksRev:=abs(lag(start)-start)]) ## find distance between peaks with the same nearest gene
+  split = lapply(split, function(x) x=x[,closestGene:=ifelse(distanceBetweenPeaks<distanceBetweenPeaksRev, distanceBetweenPeaks, distanceBetweenPeaksRev)])
+  split = lapply(split, function(x) x=x[,closestGene:=ifelse(is.na(closestGene), distanceBetweenPeaks, closestGene)])
+  split = lapply(split, function(x) x=x[,closestGene:=ifelse(is.na(closestGene), distanceBetweenPeaksRev, closestGene)])
+  distances = rbindlist(split,)
+  distances = distances %>% dplyr::select(gene, closestGene) %>% as.data.table() 
+  list = list(peak.counts, distances)
+  merged <- list %>% purrr::reduce(full_join, by = "gene")  # join all dataframes by ensembl geneID
+  merged = merged[complete.cases(merged), ]
+  enhancer.dist = merged
+}
+
+promoter = genesPerScaffold(file = "promoter_reordered_geneCoords.bed", distances = data.table())
+enhancer = genesPerScaffold(file = "enhancer_reordered_geneCoords.bed", distances = data.table())
+promoter$cre = "promoter"
+promoter.dist$cre = "promoter"
+promoter.dist = copy(distances)
+
+enhancer$cre = "enhancer"
+enhancer.dist$cre = "enhancer"
+enhancer.dist = copy(distances)
+data = rbind(promoter, enhancer)
+data=rbind(promoter.dist, enhancer.dist)
+data = data[,log10.dist:=log10(closestGene+1)]
+
+ggplot(data, aes(x=count, y=log10.dist)) + geom_point()
+
+pdf("test.pdf")
+p <- ggscatter(
+  data, x = "closestGene", y = "count",
+  color = "cre", palette = "jco", add="reg.line",
+  conf.int = TRUE, # Add confidence interval
+  cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
+  cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"), xlab = "Distance to the closest gene",
+  ylab = "Number of peaks per gene"
+  ) +
+  facet_wrap(~cre)
+p + theme(panel.spacing = unit(2, "lines"), plot.margin = margin(2, 2, 2, 2, "cm"))
+dev.off()
\ No newline at end of file
diff --git a/dunnart/scripts/genomicRegionsPlot.R b/dunnart/scripts/genomicRegionsPlot.R
new file mode 100644
index 0000000000000000000000000000000000000000..573649eb53f8b5a59b1f0c4404fb4b9c9de333c7
--- /dev/null
+++ b/dunnart/scripts/genomicRegionsPlot.R
@@ -0,0 +1,66 @@
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(ComplexHeatmap)
+library(plyr)
+
+setwd("/data/gpfs/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2")
+
+genomicRegions <- function(fileList, annotation, cpg, colors){
+    files =list.files(fileList, pattern= "_homerAnnot.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    data = lapply(data, function(x) x=setnames(x, old="CpG%", new="CpG", skip_absent=TRUE) %>% as.data.table())
+    data = lapply(data, function(x) x=setnames(x, old="GC%", new="GC", skip_absent=TRUE) %>% as.data.table())
+    names(data) = c("cluster1","cluster2","cluster3", "enhancers", "promoters")
+    subset = lapply(data, function(x) x %>% dplyr::select(Annotation, CpG, GC) %>% as.data.table())
+    df1 = Map(mutate, subset, stage = names(subset))
+    df2 = rbind(df1$cluster1, df1$cluster2, df1$cluster3, df1$promoters, df1$enhancers)
+    #df1 = rbindlist(df1,) ## bind rows back together
+    df2 = map_df(df2, ~ gsub(" ().*", "", .x))# remove everything after the first space
+    promoters_only = df2[complete.cases(df2), ]
+    tbl = with(df2, table(Annotation, stage)) %>% as.data.frame()
+    
+    # Plot proportion of peaks in each genomic region
+    pdf(annotation)
+    ggplot(tbl, aes(factor(Annotation), Freq, fill=factor(stage))) + 
+    geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
+    #geom_text(data=subset(data,Freq != 0),aes(label=Freq, y=pos), size=3)+
+    scale_fill_manual(values = colors)  + 
+    theme_minimal() + ylab("Number of peaks") +
+    xlab("")
+    dev.off()
+
+    # Plot CpG % for each stage
+    df3 = df2 %>% as.data.table()
+
+    p = ggplot(df3, aes(x=stage, y = as.numeric(CpG))) + 
+  #geom_violin(aes(fill=factor(cre), color=factor(cre)),
+  #  position = "dodge"
+  #  )+
+    geom_boxplot(aes(color=stage),
+    #outlier.shape = NA,
+    width = .2, 
+    notch = TRUE,
+    fill=colors,
+    position = position_dodge(width=.1),
+    alpha = 0.5
+    ) + theme_bw() + xlab("") + ylab("CpG %") 
+    pdf(cpg, width=9, height = 8)
+    print(p)
+    dev.off()
+}
+
+genomicRegions(fileList = "annotations/", annotation = "plots/genomicRegions_promoters2.pdf", cpg = "plots/CpG_promoters.pdf", colors =c('#4d004b','#810f7c','#88419d','#8c6bb1','#8c96c6','#9ebcda','#bfd3e6'))
+genomicRegions(fileList = "consensus/enhancers", annotation = "consensus/genomicRegions_enhancers.pdf", cpg = "consensus/CpG_enhancers.pdf", colors =c('#084081','#0868ac','#2b8cbe','#4eb3d3','#7bccc4','#a8ddb5','#ccebc5'))
+
+
diff --git a/dunnart/scripts/homerAnnot.sh b/dunnart/scripts/homerAnnot.sh
new file mode 100644
index 0000000000000000000000000000000000000000..287ce2b1cb4e5657c72925e7b2e77b38e49fd092
--- /dev/null
+++ b/dunnart/scripts/homerAnnot.sh
@@ -0,0 +1,32 @@
+# installed HOMER with the following modules loaded
+module load perl
+module load gcc
+module load web_proxy
+module load wget
+
+## GC content of promoters and enhancers using homer
+
+annotatePeaks.pl dunnart_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > dunnart_promoters_homerAnnot.txt
+annotatePeaks.pl dunnart_enhancers.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > dunnart_enhancers_homerAnnot.txt 
+
+annotatePeaks.pl enhancers/dunnart_enhancer_peaks.narrowPeak smiCra1 -gff ../../../dunnart/genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG > enhancers/dunnart_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E10.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E10.5_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E11.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E11.5_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E12.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E12.5_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E13.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E13.5_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E14.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E14.5_enhancers_homerAnnot.txt &
+annotatePeaks.pl enhancers/E15.5_enhancer_peaks.narrowPeak mm10 -CpG > enhancers/E15.5_enhancers_homerAnnot.txt &
+
+annotatePeaks.pl promoters/dunnart_promoter_peaks.narrowPeak smiCra1 -gff ../../../dunnart/genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG > promoters/dunnart_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E10.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E10.5_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E11.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E11.5_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E12.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E12.5_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E13.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E13.5_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E14.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E14.5_promoters_homerAnnot.txt &
+annotatePeaks.pl promoters/E15.5_promoter_peaks.narrowPeak mm10 -CpG > promoters/E15.5_promoters_homerAnnot.txt &
+
+## GC and CpG on promoter clusters
+annotatePeaks.pl cluster1_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > cluster1_promoters_homerAnnot.txt
+annotatePeaks.pl cluster2_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > cluster2_promoters_homerAnnot.txt
+annotatePeaks.pl cluster3_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > cluster3_promoters_homerAnnot.txt
+
diff --git a/dunnart/scripts/homerMotifEnrich_plot.R b/dunnart/scripts/homerMotifEnrich_plot.R
new file mode 100644
index 0000000000000000000000000000000000000000..d75429b1084c8106adbd83e5a7d7152bf1309d4f
--- /dev/null
+++ b/dunnart/scripts/homerMotifEnrich_plot.R
@@ -0,0 +1,250 @@
+## script used to analyse homer tfbs known motifs results and get enrichment
+library(dplyr)
+library(data.table)
+library(magrittr)
+library(ComplexHeatmap)
+library(circlize)
+library(ggthemes)
+library(ggplot2)
+library(ggpubr)
+library(viridis)
+library(viridisLite)
+library(RColorBrewer)
+library(purrr)
+library(rtracklayer)
+options(width=150)
+
+setwd('/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/')
+homer_dir = './motifEnrichment/'
+plot_dir = './motifEnrichment/'
+
+##-------------
+## read motifs
+##-------------
+read_known_motifs = function(dir){
+  known_motifs = fread(paste(homer_dir,dir,'knownResults.txt',sep=''),sep='\t',header=T)
+  known_motifs = known_motifs[,motif_name:=gsub("\\/.*","",`Motif Name`)]
+  families =stringr::str_extract(string = known_motifs$motif_name,pattern = "(?<=\\().*(?=\\))")
+  known_motifs= known_motifs[
+    ,motif_family:=families
+    ][
+      ,motif_name:=gsub("\\(.*","",motif_name)
+      ][
+        ,prop_motif_in_target:=as.numeric(gsub("\\%.*","",`% of Target Sequences with Motif`))
+        ][
+          ,c('Motif Name','Consensus','% of Target Sequences with Motif'):=NULL
+          ]
+  return(known_motifs)
+}
+
+#chimp_known = read_known_motifs('chimp_specific_pantro5/')
+#common_known = read_known_motifs('common_regions_hg38/')
+#human_known = read_known_motifs('human_specific_hg38/')
+dunnart_enhancerKnown = read_known_motifs('enhancers/')
+dunnart_promoterKnown = read_known_motifs('cluster1_promoters/')
+known_motif = list(dunnart_enhancerKnown, dunnart_promoterKnown)
+known_motif = Map(mutate,known_motif,cre=c("enhancers", "promoters"))
+number_initial_motifs = copy(known_motif)%>%lapply(function(x)as.data.table(x)[,'motif_name']%>%unique()%>%nrow())
+
+##----------------
+## filter motifs
+##----------------
+## FDR adjust pvalues
+## remove motifs with fdr adj.pval > 0.01 
+## remove motifs present in less than 5% of target sequences
+filter_motifs= function(motif){
+  filtered=copy(motif)
+  # %>%as.data.table()
+  # filtered$log_p_value[(filtered$log_p_value == Inf)] = max(filtered$log_p_value[is.finite(filtered$log_p_value)]+200)## this to counteact Inf
+  filtered=filtered[
+    ,log_pval:=ifelse(`Log P-value`< -729,-745,`Log P-value`) ## done this to control R from converting <<< 0 values to 0
+    ][
+      ,log10_fdr_adjP:=-log10(p.adjust(exp(log_pval),'fdr'))
+      ][
+        log10_fdr_adjP > 1.9
+        ][
+          prop_motif_in_target>0
+          ][
+            !motif_name %like% 'Unknown'
+            ]
+  # filtered=filtered[
+  #   p.adj< 0.01
+  #   ][
+  #     ,tgt_pct:=round(tgt_pct,2)
+  #     ][
+  #       tgt_pct>0.05
+  #       ][
+  #         !motif_name %like% 'Unknown'
+  #         ][
+  #           ,motif_name:=ifelse(motif_name %like% 'BORIS','CTCFL',motif_name)
+  #           ]
+  # filtered=tibble(filtered)
+  # names(filtered$motif_pwm)=filtered$motif_name
+  return(filtered)
+}
+
+
+filtered_known_motif = lapply(known_motif,function(x)filter_motifs(x))
+number_filtered_motifs = copy(filtered_known_motif)%>%lapply(function(x)as.data.table(x)[,'motif_name']%>%unique()%>%nrow())
+##--------------
+## plot results
+##--------------
+## important: GFY is a general factor Y, which refers to a motif for which HOMER doesnt know which if the factor that binds it (see http://homer.ucsd.edu/homer/motif/motifDatabase.html)
+## however, these motifs are real and some TFs have been described binding them (see http://genesdev.cshlp.org/content/24/14/1479.long)
+## what I'll do is to report the family for this as 'unknown' and in the text I will mention this and cite the above paper.
+motifs_table = copy(filtered_known_motif)%>%lapply(function(x)x=x[,c('motif_name','motif_family','cre','log10_fdr_adjP')])%>%rbindlist()
+motifs_table = motifs_table[,motif_family:=ifelse(motif_family=='?','Unknown',motif_family)][,motif_name_family:=paste(motif_name,motif_family,sep=':')][,c('motif_name','motif_family'):=NULL]
+## create matrix for heatmap
+matrix = copy(motifs_table)
+matrixE = matrix[!cre=="promoters"]
+matrixP = matrix[!cre=="enhancers"]
+matrixE = unique(matrixE, by = "motif_name_family") %>% as.data.table()
+matrixE = matrixE[log10_fdr_adjP>10,] %>% as.data.frame()
+matrixP = unique(matrixP, by = "motif_name_family") %>% as.data.frame()
+matrix = rbind(matrixE, matrixP) %>% as.data.table()
+matrix = dcast(matrix, motif_name_family~cre,value.var='log10_fdr_adjP') %>% as.data.table()
+
+matrix[is.na(matrix)]=0
+matrix_rownames = matrix$motif_name_family
+matrix = matrix[,motif_name_family:=NULL] %>% as.matrix()
+rownames(matrix) = matrix_rownames
+filtered_matrix = copy(matrix)
+filtered_matrix=filtered_matrix[rowSums(filtered_matrix)>10, ]
+
+filtered_matrix = filtered_matrix[!rownames(filtered_matrix) %like% 'SeqBias', ]
+rownames(filtered_matrix)[19] <- "E-box:bHLH"
+
+## get random colors for motif families 
+#n_tf_families = length(unique(gsub(".*:","",rownames(filtered_matrix))))
+#qual_col_palette = brewer.pal.info[brewer.pal.info$category == 'seq',]
+#qual_col_palette = subset(qual_col_palette, rownames(qual_col_palette) %in% c("PuRd", "YlOrRd", "Greens"))
+
+#qual_col_palette = subset(qual_col_palette, rownames(qual_col_palette) %in% c("Accent", "Set2", "Set3"))
+#col_vector = unlist(mapply(brewer.pal, qual_col_palette$maxcolors, rownames(qual_col_palette)))
+#tf_family_colors = sample(col_vector,n_tf_families)
+tf_family_colors = c("#EF476F", "#FFD166","#2a9d8f", "#ffafcc", "#F4A261", "#E76F51", "#8EcAe6", "#219EBC", "#55a630", "#9d4edd", "#b56576",
+"#0081a7", "#00AFB9", "#F07167", "#264653", "#06D6A0", "#a01a58", "#177E89", "#fec3a6", "#DB3A34", "#a5668b")
+
+tf_family_colors = c("#E1455E",
+ "#FBB055", "#FBD49D", "#F8A485","#B855A2","#85C5E0", "#95CA8A", 
+ "#2E474E", "#58A699", "#E293BF",
+"#EFD318", "#522B89", "#CA2161", "#CADF55", 
+"#0F539E","#008552", "#BA00FF", "#56ADC4", 
+"#E2E2E2","#EC468B", "#EF476F", "#a01a58", "#8EcAe6", "#ffafcc", "#F07167")
+
+names(tf_family_colors) = unique(gsub(".*:","",rownames(filtered_matrix)))
+tf_family_colors=tf_family_colors[-24:-25]
+
+## plot heatmamp
+homer_heatmap = function(x){
+  Heatmap(
+      x, 
+      border = T, 
+      col= c("#edf8b1", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#253494", "#081d58"),
+      row_dend_reorder = T,
+      # row_order=order(as.character(gsub("^.*\\.", "", rownames(enrichmatrix)))),
+      show_heatmap_legend = T,
+      heatmap_legend_param = list(title = '-log10 fdr adj P'),
+      show_row_names = T,
+      row_labels = gsub("\\:.*", "", rownames(x)),
+      row_title =" ",
+      show_column_names = T,
+      column_names_gp = gpar(fontsize = 10),
+      column_names_rot = 70,
+      column_title =' ',
+      right_annotation = HeatmapAnnotation(
+                  which='row',
+                  width = unit(1.5,'cm'),
+                  cluster = anno_simple(
+                      gsub(".*:","",rownames(x)),
+                      col= tf_family_colors
+                      ),
+                      show_annotation_name = F)
+                      )
+}
+pdf(paste(plot_dir,'homer_known_heatmap.pdf',sep=''),width= 10,height = 20)
+homer_heatmap(filtered_matrix)
+dev.off()
+##----------------------------
+## create motif logo object
+##----------------------------
+## for this read again HOMER results but using marge 
+## this allows to retrieve the PWMs for plotting
+library(marge)
+library(motifStack)
+#chimp_known_pwms = read_known_results(paste(homer_dir,'chimp_specific_pantro5/',sep=''))
+#common_known_pwms = read_known_results(paste(homer_dir,'common_regions_hg38/',sep=''))
+#human_known_pwms = read_known_results(paste(homer_dir,'human_specific_hg38/',sep=''))
+enhancer_known_pwms = read_known_results(paste(homer_dir,'enhancers/',sep=''))
+promoter_known_pwms = read_known_results(paste(homer_dir,'cluster1_promoters/',sep=''))
+enhancer_known_pwms = na.omit(enhancer_known_pwms)
+promoter_known_pwms = na.omit(promoter_known_pwms)
+my_motifs = list(enhancer_known_pwms, promoter_known_pwms)%>%lapply(function(y)y=dplyr::select(y,c('motif_name','motif_pwm'))%>%as.data.table())
+my_motifs = lapply(my_motifs,function(x)x=x[motif_name %in% gsub('\\:.*','',rownames(filtered_matrix))])%>%rbindlist()%>%tibble()%>%unique()
+names(my_motifs$motif_pwm)=my_motifs$motif_name
+motif_names=as.list(my_motifs$motif_name)
+motifs_pwm=copy(my_motifs$motif_pwm)
+get_motif_logo=function(pwm,names){
+  motifs=copy(pwm)%>%as.matrix()%>%t()
+  colnames(motifs)=paste('V',1:dim(motifs)[2],sep='')
+  motif_object= new("pcm", mat=motifs, name=names)
+  return(motif_object)
+}
+my_motif_object= purrr::map2(motifs_pwm[599],motif_names[599],function(x,y)get_motif_logo(x,y))
+pdf(paste(plot_dir,'logo_homer_top_known_gfy.pdf',sep=''),width= 15,height = 5)
+lapply(my_motif_object,function(x)plot(x))
+dev.off()
+
+## now make a barplot with proportion motif families
+
+enhancer = data.frame(motif_name_family = row.names(filtered_matrix), filtered_matrix[,1])
+names(enhancer) <- c("motif_name_family", "logpvalue")
+enhancer = enhancer[enhancer$logpvalue>0,]
+enhancer[118] <- "RBPJ:bHLH"
+
+promoter = data.frame(motif_name_family = row.names(filtered_matrix), filtered_matrix[,2])
+names(promoter) <- c("motif_name_family", "logpvalue")
+promoter = promoter[promoter$logpvalue>0,]
+
+motif_families=data.table(motif_name_family=rownames(filtered_matrix))
+promoter = data.table(motif_name_family=promoter$motif_name_family)
+enhancer = data.table(motif_name_family=enhancer$motif_name_family)
+
+motif_families = motif_families[
+  ,c("motif_name", "motif_family") := tstrsplit(motif_name_family, ":", fixed=TRUE)
+  ][
+    ,'motif_name_family':=NULL
+    ][
+      ,numb_motifs_by_family:=.N,by=.(motif_family)
+      ][
+        ,numb_motifs:=.N
+        ][
+          ,prop_family:=numb_motifs_by_family/numb_motifs
+          ][
+            ,mock_col:='tf_family'
+          ]
+
+motif_families = enhancer[
+  ,c("motif_name", "motif_family") := tstrsplit(motif_name_family, ":", fixed=TRUE)
+  ][
+    ,'motif_name_family':=NULL
+    ][
+      ,numb_motifs_by_family:=.N,by=.(motif_family)
+      ][
+        ,numb_motifs:=.N
+        ][
+          ,prop_family:=numb_motifs_by_family/numb_motifs
+          ][
+            ,mock_col:='tf_family'
+          ]%>% setorderv("prop_family",1)
+
+prop_motif_families = copy(motif_families)[,c('prop_family','motif_family','mock_col')]%>%unique()
+
+prop_motif_families <- prop_motif_families[order(prop_motif_families$prop_family, decreasing = TRUE),] # (1)
+prop_motif_families$prop_familyFactor <-factor(prop_motif_families$prop_family, levels= unique(prop_motif_families$prop_family) ) # (2)
+
+pdf(paste(plot_dir,'prop_families_homer_top_known_motifs_promoters.pdf',sep=''),width= 5,height = 10)
+ggplot(prop_motif_families,aes(x=mock_col,y=prop_family,fill=motif_family, group=prop_familyFactor))+
+geom_bar(stat="identity")+
+scale_fill_manual(values = tf_family_colors)+ theme_bw()
+dev.off()
\ No newline at end of file
diff --git a/dunnart/scripts/homer_motifEnrich.sh b/dunnart/scripts/homer_motifEnrich.sh
new file mode 100644
index 0000000000000000000000000000000000000000..199d5d3954aaa216822aa0298b258f6a2f5326b0
--- /dev/null
+++ b/dunnart/scripts/homer_motifEnrich.sh
@@ -0,0 +1,26 @@
+## Laura Cook
+## Motif enrichment with HOMER
+
+# installed HOMER with the following modules loaded
+module load perl
+module load gcc
+module load web_proxy
+module load wget
+
+
+## Load genome for use with HOMER tools
+## This will allow the genome to be called within the tools
+loadGenome.pl -name smiCra1 -org null -fasta /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fasta -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff
+
+## analyse genomic regions for de novo and known motifs
+## find enrichment compared to a random expectation based on 
+## default fragment size for motif finding is 200bp
+findMotifsGenome.pl dunnart_enhancers.narrowPeak smiCra1 motifEnrichment/enhancers/ -size given -p 32 -preparse -mknown /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/homer/data/knownTFs/vertebrates/known.motifs
+
+findMotifsGenome.pl dunnart_promoters.narrowPeak smiCra1 motifEnrichment/promoters/ -size given -p 32 -preparse -mknown /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/homer/data/knownTFs/vertebrates/known.motifs
+
+findMotifsGenome.pl cluster1_promoters.narrowPeak smiCra1 motifEnrichment/cluster1_promoters/ -size given  -p 32 -preparse -mknown /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/homer/data/knownTFs/vertebrates/known.motifs
+
+findMotifsGenome.pl cluster1_promoters.narrowPeak smiCra1 motifEnrichment/cluster1_promoters_polII/ -size given -mknown /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/homer/data/knownTFs/motifs/groups/promoter.motifs  -p 32 -preparse
+
+findMotifsGenome.pl dunnart_enhancers.narrowPeak smiCra1 motifEnrichment/enhancers_polII/ -size given -mknown /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/bin/homer/data/knownTFs/motifs/groups/promoter.motifs  -p 32 -preparse
diff --git a/dunnart/scripts/overlap_peaks.py b/dunnart/scripts/overlap_peaks.py
old mode 100644
new mode 100755
index fe36e8a592611c9e3fb0566f43eaf7a84fdf8575..b788ca7c7fdb54fbe37c5a7049d201a989a9e67b
--- a/dunnart/scripts/overlap_peaks.py
+++ b/dunnart/scripts/overlap_peaks.py
@@ -1,13 +1,10 @@
 #! /usr/bin/env python
 
-## Author: Laura E Cook, University of Melbourne
 ## Purpose:
 #   1. Import replicate 1, replicate 2, and output file name from snakemake rule
-#   2. Concatonate peak 1 adn peak 2 to get a pooled_peaks file
-#   3. Intersect pooled peaks with peak 1
-#   4. Write the original A and B entries plus the number of base pairs of overlap between the two features. Only A features with overlap are reported.
-#   5. Calculate
-#   5.
+#   2. Intersect pooled peaks with peak 1
+#   3. Write the original A and B entries plus the number of base pairs of overlap between the two features. Only A features with overlap are reported.
+
 
 import sys
 import os
diff --git a/dunnart/scripts/peakAnno.R b/dunnart/scripts/peakAnno.R
new file mode 100755
index 0000000000000000000000000000000000000000..1481c91347c86a8af39b368fbc3648c703c07474
--- /dev/null
+++ b/dunnart/scripts/peakAnno.R
@@ -0,0 +1,107 @@
+library(GenomicFeatures)
+library(GenomicRanges)
+library(rtracklayer)
+library(AnnotationForge)
+library(clusterProfiler)
+library(ChIPseeker)
+library(ggpubr)
+library(AnnotationDbi)
+library(AnnotationHub)
+library(org.Mm.eg.db)
+library(plyr)
+library(ChIPpeakAnno)
+library(tidyverse)
+library(RColorBrewer)
+library(ggplot2)
+
+## Make txdb for dunnart annotation file
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2")
+smiCra_txdb <- makeTxDbFromGFF("Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff")
+
+
+## geneID conversion tables
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+df2 <- read.table("biomart_ensembl.txt", header=TRUE, sep="\t") ## conversion table for devil ENSEMBL to mouse ENSEMBL
+df3 <- read.table("bioDBnet_refseq2ensembl_H3K27ac.txt", header=TRUE, sep="\t") ## convertsion table for devil refseq to devil ENSEMBL
+
+#################################################### DUNNART ANNOTATION ##########################################################
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2")
+
+## Anotate peak files
+annotatePeaks <- function(peak, outFile, outFile1, annoBar, distanceTSS, heatmapTSS, GOenrich, kegg){
+  
+  # Annotate peak file based on dunnart GFF
+  peakAnno <- annotatePeak(peak, tssRegion = c(-3000, 3000), TxDb = smiCra_txdb)
+  
+  #Plot annotation
+  # pdf(file=annoBar, width=10, height=7)
+  # print(plotAnnoBar(peakAnno))
+  # dev.off()
+  
+  # Set promoter range
+  # promoter <- getPromoters(TxDb=smiCra_txdb, upstream=3000, downstream=3000)
+  
+  # # Find distance from TSS
+  # tagMatrix <- getTagMatrix(toGRanges(peak), windows=promoter)
+  
+  # # Plot heatmap on peak distance to TSS
+  # pdf(file=heatmapTSS, width=10, height=7)
+  # print(tagHeatmap(tagMatrix, xlim=c(-3000, 3000), color = "#6699CC"))
+  # dev.off()
+
+  # Plot distance from TSS
+  # pdf(file=distanceTSS, width=10, height=7)
+  # print(plotAvgProf(tagMatrix, xlim=c(-300000, 300000)))
+  # dev.off()
+
+  # Write annotation to file
+  write.table(peakAnno, outFile, sep="\t", quote=F, row.names=F)
+  
+  peakAnnoDF <- as.data.frame(peakAnno, row.names = NULL)
+  # # Convert refseq IDs and geneIDs to devil ensembl IDs
+  peakAnnoDF$ensemblgeneID <- df2$Gene.stable.ID[match(unlist(peakAnnoDF$geneId), df2$Gene.name)]
+  peakAnnoDF$ensemblgeneID = replace(peakAnnoDF$ensemblgeneID,is.na(refseqID),"-")
+  peakAnnoDF$transcriptIdAltered <- gsub("\\..*","", peakAnnoDF$transcriptId)
+  peakAnnoDF$refseqID <- df3$Ensembl.Gene.ID[match(unlist(peakAnnoDF$transcriptIdAltered), df3$RefSeq.mRNA.Accession)]
+  peakAnnoDF$refseqID = replace(refseqID$ensemblgeneID,is.na(refseqID),"-")
+  peakAnnoDF$combined <- ifelse(peakAnnoDF$refseqID == "-", peakAnnoDF$ensemblgeneID, peakAnnoDF$refseqID)
+  peakAnnoDF$refseqID[peakAnnoDF$refseqID == as.character("-")] <- NA
+  
+  # # Convert devil ensembl to mouse ensembl
+  peakAnnoDF$mouseensembl <- df2$Mouse.gene.stable.ID[match(unlist(peakAnnoDF$combined), df2$Gene.stable.ID)]
+  # # Write annotation with converted IDs 
+  peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$mouseensembl),]
+  write.table(peakAnnoDF, outFile1, sep="\t", quote=F, row.names=F)
+
+  # # GO enrichment analysis
+  GO <- enrichGO(gene = peakAnnoDF$mouseensembl,
+                  keyType = "ENSEMBL",
+                  OrgDb = org.Mm.eg.db,
+                  ont = "BP",
+                  pAdjustMethod = "BH",
+                  qvalueCutoff = 0.05,
+                  readable = TRUE)
+   write.table(GO, GOenrich, sep="\t", quote=F, row.names=F)
+  
+  # ## convert mouse ensembl to entrezID
+  suppressWarnings(gene.df <- bitr(geneID=peakAnnoDF$mouseensembl, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" ))
+  
+  # ## kegg analysis
+  kk <- enrichKEGG(gene = gene.df$ENTREZID, organism = "mmu", pvalueCutoff=0.05)
+  write.table(kk, kegg, sep="\t", quote=F, row.names=F)
+}
+
+
+### Consensus peaks
+annotatePeaks(peak = "dunnart_enhancers.narrowPeak", outFile = "dunnart_enhancer_annotation.txt", annoBar = "dunnart_enhancer_annoBar.pdf", distanceTSS = "dunnart_enhancer_distanceTSS.pdf", heatmapTSS = "dunnart_enhancer_heatmapTSS.pdf", outFile1 = "dunnart_enhancer_annotationConvertedIDs.txt", GOenrich = "enhancer_mm10GOenrich.txt", kegg="enhancer_KEGG.txt")
+annotatePeaks(peak = "dunnart_promoters.narrowPeak", outFile = "dunnart_promoter_annotation.txt", annoBar = "dunnart_promoter_annoBar.pdf", distanceTSS = "dunnart_promoter_distanceTSS.pdf", heatmapTSS = "dunnart_promoter_heatmapTSS.pdf", outFile1 = "dunnart_promoter_annotationConvertedIDs.txt" , GOenrich = "promoter_mm10GOenrich.txt", kegg="promoter_KEGG.txt")
+annotatePeaks(peak = "cluster1_promoters.narrowPeak", outFile = "cluster1_promoter_annotation.txt", annoBar = "cluster1_promoter_annoBar.pdf", distanceTSS = "cluster1_promoter_distanceTSS.pdf", heatmapTSS = "cluster1_promoter_heatmapTSS.pdf", outFile1 = "cluster1_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster1_mm10GOenrich.txt", kegg="cluster1_KEGG.txt")
+annotatePeaks(peak = "cluster2_promoters.narrowPeak", outFile = "cluster2_promoter_annotation.txt", annoBar = "cluster2_promoter_annoBar.pdf", distanceTSS = "cluster2_promoter_distanceTSS.pdf", heatmapTSS = "cluster2_promoter_heatmapTSS.pdf", outFile1 = "cluster2_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster2_mm10GOenrich.txt", kegg="cluster2_KEGG.txt")
+annotatePeaks(peak = "cluster3_promoters.narrowPeak", outFile = "cluster3_promoter_annotation.txt", annoBar = "cluster3_promoter_annoBar.pdf", distanceTSS = "cluster3_promoter_distanceTSS.pdf", heatmapTSS = "cluster3_promoter_heatmapTSS.pdf", outFile1 = "cluster3_promoter_annotationConvertedIDs.txt" , GOenrich = "cluster3_mm10GOenrich.txt", kegg="cluster3_KEGG.txt")
+
+annotatePeaks(peak = "H3K4me3_overlap.narrowPeak", outFile = "H3K4me3_annotation.txt", annoBar = "H3K4me3_overlap_annoBar.pdf", distanceTSS = "H3K4me3_overlap_distanceTSS.pdf", heatmapTSS = "H3K4me3_overlap_heatmapTSS.pdf", outFile1 = "H3K4me3_overlap_annotationConvertedIDs.txt", GOenrich = "H3K4me3_overlap_mm10GOenrich", kegg="H3K4me3_overlap_KEGG.txt")
+annotatePeaks(peak = "H3K27ac_overlap.narrowPeak", outFile = "H3K27ac_annotation.txt", annoBar = "H3K27ac_overlap_annoBar.pdf", distanceTSS = "H3K27ac_overlap_distanceTSS.pdf", heatmapTSS = "H3K27ac_overlap_heatmapTSS.pdf", outFile1 = "H3K27ac_overlap_annotationConvertedIDs.txt" , GOenrich = "H3K27ac_overlap_mm10GOenrich", kegg="H3K27ac_overlap_KEGG.txt")
+
+annotatePeaks(peak = "H3K4me3_and_H3K27ac.narrowPeak", outFile = "H3K4me3_and_H3K27ac_annotation.txt")
+annotatePeaks(peak = "H3K4me3_only.narrowPeak", outFile = "H3K4me3_only_annotation.txt")
+annotatePeaks(peak = "H3K27ac_only.narrowPeak", outFile = "H3K27ac_only_annotation.txt")
\ No newline at end of file
diff --git a/dunnart/scripts/peakFeatures.R b/dunnart/scripts/peakFeatures.R
new file mode 100755
index 0000000000000000000000000000000000000000..c61f422b62db0febbca23dca02edde2a56db7739
--- /dev/null
+++ b/dunnart/scripts/peakFeatures.R
@@ -0,0 +1,523 @@
+## This script looks through various features of the dunnart peaks including
+## Peak intensity, lengths, distance to TSS, filtering peaks.
+
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(multcompView)
+library(factoextra)
+library(NbClust)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/")
+plot_dir = "plots/"
+fullPeak_dir = "fullPeaks/"
+annot_dir = "annotations/"
+filterPeaks_dir = "filterPeaks/"
+
+font = theme(axis.text.x = element_text(size = 25),
+        axis.text.y = element_text(size = 25),  
+        axis.title.x = element_text(size = 25),
+        axis.title.y = element_text(size = 25), 
+        legend.title=element_text(size=25), legend.text=element_text(size=25))
+
+
+###########################################################################################################
+############################################## Peak Features ##############################################
+###########################################################################################################
+
+unfilteredPeakFeatures <- function(fileList, combined.tbl.stack, peak.length.plot, peak.intensity.plot){
+    files =list.files(fileList, pattern= ".narrowPeak", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")))
+    names(data) = c("enhancers","promoters","H3K27ac_only","H3K27ac","H3K4me3_H3K27ac", "H3K4me3_only","H3K4me3")
+    df1 = Map(mutate, data, group = names(data))
+    df1$H3K27ac_only$cre = "enhancers"
+    df1$H3K4me3_only$cre = "promoters"
+    df1$H3K4me3_H3K27ac$cre = "promoters"
+
+    fullPeaks = rbind(df1$enhancers, df1$promoters, df1$H3K27ac, df1$H3K4me3)
+    combinedPeaks = rbind(df1$H3K27ac_only, df1$H3K4me3_H3K27ac, df1$H3K4me3_only)
+        
+    ## Plot stacked bar graph with number of peaks for each histone mark
+    combined.tbl <- with(combinedPeaks, table(group, cre))
+    combined.tbl <- as.data.frame(combined.tbl)
+    pdf(file=paste0(plot_dir, combined.tbl.stack), width = 10, height = 7)
+      ggplot(combined.tbl, aes(factor(cre), Freq, fill=group)) + 
+      geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
+      scale_fill_manual(values = c("#FF9D40",  "#37B6CE", "#04859D"))  + 
+      theme_minimal() + ylab("Number of peaks") +
+      xlab("")
+    dev.off()
+
+    # Peak lengths for H3K4me3 and H3K27ac
+    fullPeaks$length <- (fullPeaks$V3 - fullPeaks$V2)
+    level_order <- c('promoter', 'enhancer', 'H3K4me3', 'H3K27ac')
+
+    ## Plot peak lengths
+    p <- ggplot(fullPeaks, aes(factor(group, level = level_order), y = log10(length))) + 
+    geom_half_violin(aes(fill=factor(group), color=factor(group)),
+    side = "r", 
+    position = "dodge"
+    )+
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#FCF8EC","#F5F6F7","#E4F3F1"),
+    position = position_dodge(width=.1)
+    ) +
+    coord_cartesian(xlim = c(1.2, NA), clip = "off") + theme_bw() + xlab("") + ylab("Log10 Peak Length") 
+    pdf(file=paste0(plot_dir, peak.length.plot), width = 10, height = 7)
+    print(p +  scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
+    scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
+    dev.off()
+
+    ## Plot peak intensity
+    p <- ggplot(fullPeaks, aes(factor(group, level = level_order), y = log10(V7))) + 
+    geom_half_violin(aes(fill=factor(group), color=factor(group)),
+    side = "r", 
+    position = "dodge"
+    )+
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#FCF8EC","#F5F6F7","#E4F3F1"),
+    position = position_dodge(width=.1)
+    ) +
+    coord_cartesian(xlim = c(1.2, NA), clip = "off") + theme_bw() + xlab("") + ylab("Log10 Peak Intensity") 
+    pdf(file=paste0(plot_dir, peak.intensity.plot), width = 10, height = 7)
+    print(p +  scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
+    scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
+    dev.off()  
+}
+
+unfilteredPeakFeatures(fileList = fullPeak_dir, combined.tbl.stack= "dunnart_combined_stacked.pdf",
+peak.length.plot = "dunnart_fullPeaks_length.pdf", peak.intensity.plot = "dunnart_fullPeaks_intensity.pdf")
+
+## Now see where the peaks are located in relation to the TSS
+## Promoters should be reasonably close to the TSS and enhancers more distal to the TSS
+## Plot distance to TSS for all unfiltered peaks 
+
+distanceToTSS <- function(fileList, enhancerTSSplot, promoterTSSplot, TSSplot){
+    files =list.files(fileList, pattern= "annotation.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(data) = c("enhancers","promoters","H3K27ac","H3K4me3")
+    df1 = Map(mutate, data, mark = names(data))
+    enhProm = list(df1$promoters, df1$enhancers)
+    enhProm = suppressWarnings(lapply(enhProm, function(x) x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
+    names(enhProm) = c("promoters","enhancers")
+
+    pdf(file = paste0(plot_dir, enhancerTSSplot), width=10, height = 7)
+    print(ggplot(enhProm$enhancers, aes(x=log10_abs_dist)) + 
+    geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + 
+    scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw())
+    dev.off()
+
+    pdf(file = paste0(plot_dir, promoterTSSplot), width=10, height = 7)
+    print(ggplot(enhProm$promoters, aes(x=log10_abs_dist)) + 
+    geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + 
+    scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw())
+    dev.off()
+
+    enhProm = rbindlist(enhProm,)
+    pdf(file = paste0(plot_dir, TSSplot), width=10, height = 7)
+    print(ggplot(enhProm, aes(x=log10_abs_dist, fill=factor(mark), color=factor(mark))) + 
+    geom_density(alpha=0.5) +scale_color_manual(values=c("#E9C46A", "#2A9D8F")) + scale_fill_manual(values=c("#E9C46A", "#2A9D8F")) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw() + font)
+    dev.off()
+
+}
+distanceToTSS(fileList = annot_dir, enhancerTSSplot="dunnart_enhancerTSS.pdf", 
+promoterTSSplot="dunnart_promoterTSS.pdf", TSSplot = "dunnart_TSS.pdf")
+
+#########################################################################################################################
+################################################### K-MEAN CLUSTERING ###################################################
+#########################################################################################################################
+### From this we can see that the promoter peaks have a large amount of peaks a long way from the TSS
+### Suggests that these are either actually enhancers or they represent unannotated transcripts
+### Probably a mixture of both based on comparison with mouse peaks (where the annotation is better) there are not as many
+### peaks in this distal regions. 
+### Use k-means clustering to group the peaks to decide on a cut off for "promoter" peaks. This will be more conservative for
+### what we identify as promoters.
+
+font = theme(axis.text.x = element_text(size = 25),
+        axis.text.y = element_text(size = 25),  
+        axis.title.x = element_text(size = 25),
+        axis.title.y = element_text(size = 25), 
+        legend.title=element_text(size=25), legend.text=element_text(size=25))
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/")
+
+## k-means clustering 
+
+kmeansCluster <- function(file, plot1, plot2, output){
+  data = fread(file, header=TRUE, sep="\t", quote = "") # read in all files
+  data = data[,log10_dist:=log10(abs(distanceToTSS)+1.1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
+  data = data[,abs_dist:=log10(abs(distanceToTSS)+1.1)]
+
+  data = data %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation")
+  
+  ## plot the number of peaks in each cluster
+  ## Using the MacQueen algorithm instead of the default Lloyd 
+  ## The algorithm is more efficient as it updates centroids more often and usually needs to
+  ## perform one complete pass through the cases to converge on a solution.
+  df = data[,5]
+  cre.kmeans = kmeans(df, 3, iter.max=100, nstart=25, algorithm="MacQueen")  
+  cre.kmeans_table = data.frame(cre.kmeans$size, cre.kmeans$centers)
+  cre.kmeans_df = data.frame(Cluster = cre.kmeans$cluster, data)
+
+  pdf(plot1, width=10, height = 7)
+  p <- ggplot(data = cre.kmeans_df, aes(y = Cluster, fill=as.factor(Cluster), color=as.factor(Cluster))) +
+  geom_bar()  +
+  theme(plot.title = element_text(hjust = 0.5)) + theme_bw() + font
+  pdf(plot1)
+  print(p + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) + scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")))
+  dev.off()
+
+
+  p <- ggplot(cre.kmeans_df, aes(x=log10_dist, fill=as.factor(Cluster), color=as.factor(Cluster))) + 
+            geom_histogram(binwidth=0.1, position = 'identity')  +
+            theme_bw()+ font
+  pdf(plot2, width = 10, height = 7)
+  print(p + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) + scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")))
+  dev.off()  
+
+  write.table(cre.kmeans_df, output, sep="\t", quote=F, row.names=F)
+  return(cre.kmeans_df)
+}
+
+promoter_kmeans = kmeansCluster(file = paste0(annot_dir, "dunnart_promoter_annotation.txt"),  plot1 = paste0(plot_dir, "promoter_kmeans_bar.pdf"), plot2 = paste0(plot_dir, "promoter_kmeans_histogram.pdf"), output = paste0(annot_dir, "promoter_kmeans_peaks.txt"))
+enhancer_kmeans = kmeansCluster(number_of_clusters = 2, file = paste0(annot_dir, "dunnart_enhancer_annotation.txt"),  plot1 = paste0(plot_dir, "enhancer_kmeans_bar.pdf"), plot2 = paste0(plot_dir, "enhancer_kmeans_histogram.pdf"), output = paste0(annot_dir, "enhancer_kmeans_peaks.txt"))
+
+## Filter for cluster 1 and make final promoter/enhancer plots
+
+plotPeakClusters <- function(x, y, enhancer, promoter, plotTSS, peakIntensityPlot, peakWidthPlot, annotation){
+  enhancer.annot= fread(enhancer, header=TRUE, sep="\t", quote = "") 
+  enhancer.annot = enhancer.annot[,log10_dist:=log10(abs(distanceToTSS)+1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
+  enhancer.annot = enhancer.annot[,abs_dist:=abs(distanceToTSS)]
+  promoter.annot = fread(promoter, header=TRUE, sep="\t", quote = "")
+  promoter.annot = promoter.annot[,log10_dist:=log10(abs(distanceToTSS)+1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
+  promoter.annot = promoter.annot[,abs_dist:=abs(distanceToTSS)]
+
+  promoter.annot$label = "promoter"
+  enhancer.annot$label = "enhancer"
+  enhancer.annot = enhancer.annot %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation", "label")
+  promoter.annot = promoter.annot %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation", "label")
+
+  promoter.kmeans = fread(x, header=TRUE, sep="\t", quote = "") 
+  promoter.kmeans$cre = "promoter"
+  enhancer.kmeans = fread(y, header=TRUE, sep="\t", quote = "") 
+  enhancer.kmeans$cre = "enhancer"
+  data = rbind(enhancer.kmeans, promoter.kmeans)
+
+  for (i in 1:nrow(data)) {
+    if(data$Cluster[i]=='1' & data$cre[i]=='enhancer'){
+      data[i, 'label'] <- "enhancer cluster 1"
+      }
+      else if(data$Cluster[i]=='2' & data$cre[i]=='enhancer') {
+        data[i, 'label'] <- "enhancer cluster 2"
+        }
+        else if(data$Cluster[i]=='3' & data$cre[i]=='enhancer') {
+          data[i, 'label'] <- "enhancer cluster 3"
+          }
+          else if(data$Cluster[i]=='1' & data$cre[i]=='promoter') {
+            data[i, 'label'] <- "promoter cluster 1"
+            }
+            else if(data$Cluster[i]=='2' & data$cre[i]=='promoter') {
+              data[i, 'label'] <- "promoter cluster 2"
+              }
+              else if(data$Cluster[i]=='3' & data$cre[i]=='promoter') {
+                data[i, 'label'] <- "promoter cluster 3"
+                }
+  }
+
+  data2 = data %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation", "label")
+  all_peaks = rbind(data2, enhancer.annot, promoter.annot)
+  all_peaks = as.data.table(all_peaks)
+  all_peaks = all_peaks[,log10_intensity:=log10(V7)]
+  all_peaks = all_peaks[,log10_width:=log10(width)]
+
+  colors = c("#E9C46A","#e34a33", "#fdbb84","#fee8c8", "#2A9D8F","#8856a7", "#9ebcda","#e0ecf4")
+ 
+  ## Plot distance to TSS
+  pdf(plotTSS, width=10, height = 7)
+  print(ggplot(data, aes(x=log10_dist, colour = factor(Cluster), fill=factor(Cluster))) + geom_density(aes(x = log10_dist, y = ..density..), position="stack", alpha=0.7) + 
+  theme_bw() + scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B", "#2A9D8F"))+ 
+  scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B", "#2A9D8F")) + font)
+  dev.off()
+
+  
+  ## Plot peak intensity
+  p <- ggplot(all_peaks, aes(factor(label), y = log10_intensity)) + 
+  geom_violin(aes(fill=factor(label), color=factor(label)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(label)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("Log 10 peak intensity") 
+  pdf("test.pdf", width=10, height = 8)
+  print(p + scale_color_manual(values = colors) +
+  scale_fill_manual(values = colors) + font) 
+  dev.off()
+
+  ## Plot peak width
+  p <- ggplot(all_peaks, aes(factor(label), y = log10_width)) + 
+  geom_violin(aes(fill=factor(label), color=factor(label)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(label)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#E7EEF6","#D4C7E2","#D3BFD2", "#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("Log 10 peak width") 
+  pdf(peakWidthPlot, width=9, height = 8)
+  print(p + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B", "#2A9D8F")) +
+  scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B", "#2A9D8F")) + font)
+  dev.off()
+
+  ## Plot genomic regions
+  df2 = rbind(promoter_kmeans)
+    #df1 = rbindlist(df1,) ## bind rows back together
+  df2 = map_df(df2, ~ gsub(" ().*", "", .x))# remove everything after the first space
+  #promoters_only = df2[complete.cases(df2), ]
+  tbl = with(df2, table(annotation, Cluster)) %>% as.data.frame()
+  pdf(annotation)
+    ggplot(tbl, aes(factor(annotation), Freq, fill=factor(Cluster))) + 
+    geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
+    #geom_text(data=subset(data,Freq != 0),aes(label=Freq, y=pos), size=3)+
+    scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B"))  + 
+    theme_minimal() + ylab("Number of peaks") +
+    xlab("")
+    dev.off()
+}
+plotPeakClusters(peakWidthPlot = paste0(plot_dir, "peakWidthPlot.pdf"),plotTSS =paste0(plot_dir,  "peakDistanceToTSS.pdf"),
+peakIntensityPlot = paste0(plot_dir, "peakIntensityPlot.pdf"), annotation = paste0(plot_dir, "peakAnnotation.pdf"),
+x= paste0(annot_dir, "promoter_kmeans_peaks.txt"), y = paste0(annot_dir,"enhancer_kmeans_peaks.txt"), 
+enhancer = paste0(annot_dir, "dunnart_enhancer_annotation.txt"), promoter = paste0(annot_dir, "dunnart_promoter_annotation.txt"))
+
+## Extract cluster groups from narrowPeak files and save separately
+cluster1 <- promoter_kmeans$V4[promoter_kmeans$Cluster==1]
+cluster2 <- promoter_kmeans$V4[promoter_kmeans$Cluster==2]
+cluster3 <- promoter_kmeans$V4[promoter_kmeans$Cluster==3]
+
+cluster1 <- enhancer_kmeans$V4[enhancer_kmeans$Cluster==1]
+cluster2 <- enhancer_kmeans$V4[enhancer_kmeans$Cluster==2]
+cluster3 <- enhancer_kmeans$V4[enhancer_kmeans$Cluster==3]
+
+
+
+extractClusters = function(promoter, cluster1, cluster2, cluster3, out1, out2, out3){
+  file= fread(promoter, header=FALSE, sep="\t", quote = "") 
+  write.table(subset(file, V4 %in% cluster1), out1, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+  write.table(subset(file, V4 %in% cluster2), out2, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+  write.table(subset(file, V4 %in% cluster3), out3, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+}
+
+extractClusters(promoter = "dunnart_promoters.narrowPeak", out1 = "cluster1_promoters.narrowPeak", 
+out2 ="cluster2_promoters.narrowPeak" , out3 = "cluster3_promoters.narrowPeak", cluster1 = cluster1, 
+cluster2 = cluster2, cluster3 = cluster3)
+
+extractClusters(promoter = "dunnart_enhancers.narrowPeak", out1 = "cluster1_enhancers.narrowPeak", 
+out2 ="cluster2_enhancers.narrowPeak" , out3 = "cluster3_enhancers.narrowPeak", cluster1 = cluster1, 
+cluster2 = cluster2, cluster3 = cluster3)
+
+
+## Now see where the peaks are located in relation to the TSS
+## Promoters should be reasonably close to the TSS and enhancers more distal to the TSS
+## Plot distance to TSS for all unfiltered peaks 
+
+distanceToTSS <- function(fileList, enhancerTSSplot, promoterTSSplot, TSSplot){
+    files =list.files(fileList, pattern= "annotation.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(data) = c("cluster1", "cluster2", "cluster3", "enhancers","promoters","H3K27ac","H3K4me3")
+    enhProm = Map(mutate, data, mark = names(data))
+    #promoter_5kb = x
+    #promoter_5kb$mark = "promoter_5kb"
+    #enhProm = list(df1$enhancers, df1$promoters)
+    enhProm = suppressWarnings(lapply(enhProm, function(x) x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
+    enhProm = suppressWarnings(lapply(enhProm, function(x) x[,abs_dist:=abs(distanceToTSS)]))
+    #names(enhProm) = c("enhancers","promoters", "promoter_5kb")
+    level_order = c("cluster1", "cluster2", "cluster3", "promoters", "enhancers", "H3K4me3", "H3K27ac")
+
+    pdf(file = paste0(plot_dir, enhancerTSSplot), width=10, height = 7)
+    print(ggplot(enhProm$enhancers, aes(x=log10_abs_dist, color=factor(mark), fill=factor(mark))) + 
+    geom_density(alpha=0.5) + theme_bw() + scale_color_manual(values = c('#E9C46A')) 
+    + scale_fill_manual(values = c('#E9C46A')) + font) 
+    dev.off()
+
+    enhProm = rbind(enhProm$promoters, enhProm$cluster1, enhProm$cluster2, enhProm$cluster3 ,use.names=TRUE)
+    pdf(file = paste0(plot_dir, TSSplot), width=15, height = 7)
+    print(ggplot(enhProm, aes(x=log10_abs_dist, color = factor(mark, level=level_order), fill = factor(mark, level=level_order))) + 
+    geom_density(position = "stack", alpha=0.8) + 
+    theme_bw() )
+    #+ scale_color_manual(values = c('#4D004B', '#E9C46A','#2A9D8F')) 
+    #+ scale_fill_manual(values = c('#4D004B', '#E9C46A','#2A9D8F')) + font) 
+    dev.off()
+}
+distanceToTSS(fileList = annot_dir, enhancerTSSplot="dunnart_enhancerTSS.pdf", promoterTSSplot="dunnart_promoterTSS.pdf",
+TSSplot = "dunnart_TSS.pdf")
+
+
+## Now look at CpG% and GC% for these groups
+## Run Homer on the clusters 
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/")
+
+GCcontent = function(fileList, gcPlot, cpgPlot, dataframestobind){
+    files =list.files(fileList, pattern= "homerAnnot.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(data) = c("cluster1","cluster2","cluster3","enhancers", "promoters")
+    df1 = Map(mutate, data, group = names(data))
+    df1 = lapply(df1, function(x) x %>% dplyr::select("Chr", "Start", "End", "Peak Score", "Distance to TSS", "CpG%", "GC%", "group") %>% as.data.table())
+    df1 = rbind(df1$cluster1, df1$cluster2, df1$cluster3, df1$promoters, df1$enhancers)
+    colnames(df1)[5:7] <- c("distanceToTSS","CpG", "GC")
+    df1[is.na(df1)] <- 0
+    df1 = df1[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+
+  p <- ggplot(df1, aes(factor(group), y = GC)) + 
+    geom_violin(aes(fill=factor(group), color=factor(group)),
+    position = "dodge"
+    )+
+  geom_boxplot(aes(color=factor(group)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#E7EEF6","#D4C7E2","#D3BFD2", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("GC content") 
+  pdf(gcPlot, width=9, height = 8)
+  print(p + font + scale_color_manual(values = c('#9EBCDA','#8C6BB1','#4D004B', '#E9C46A','#2A9D8F')) +
+  scale_fill_manual(values = c("#B6CDE3","#A990C4","#7A4078" ,"#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" ))
+  dev.off()
+
+  p <- ggplot(df1, aes(factor(group), y = CpG)) + 
+  geom_violin(aes(fill=factor(group), color=factor(group)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(group)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#E7EEF6","#D4C7E2","#D3BFD2", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("CpG") 
+  pdf(cpgPlot, width=9, height = 8)
+  print(p + font + scale_color_manual(values = c('#9EBCDA','#8C6BB1','#4D004B', '#E9C46A','#2A9D8F')) +
+  scale_fill_manual(values = c("#B6CDE3","#A990C4","#7A4078" ,"#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" ))
+  dev.off()
+}
+GCcontent(fileList = annot_dir, gcPlot = paste0(plot_dir, "dunnart_GC.pdf"), cpgPlot = paste0(plot_dir, "dunnart_cpg.pdf"))
+
+
+# Peak length vs distance from TSS and peak intensity vs distance from TSS
+peak.counts <- df2 %>% as.data.frame() %>% group_by(geneId, group, geneLength) %>%
+    summarise(count = n(), .groups = 'drop') %>% as.data.table()
+
+pdf("geneLengthvsPeakCount.pdf", width=10, height = 15)
+p <- ggscatter(
+  peak.counts, x = "count", y = "geneLength",
+  color = "group", palette = "jco", add="reg.line",
+  conf.int = TRUE, # Add confidence interval
+  cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
+  cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"), xlab = "Gene Length",
+  ylab = "Number of peaks per gene"
+  ) +
+  facet_wrap(~group)
+p + theme(panel.spacing = unit(3, "lines"), plot.margin = margin(2, 2, 2, 2, "cm"))
+dev.off()
+
+pdf("peakLengthVsDist.pdf", width=10, height = 7)
+ggplot(peak, aes(abs(distanceToTSS), y=log10_length, color=cre)) + geom_point(alpha=0.3) +  theme_bw() + scale_color_manual(values=c("#F1DAA2", "#7AC2B9"))
+dev.off()
+
+
+## Peak intensity for distal enhancers marked by H3K4me3
+# subset log10_abs_dist between 3.5 and 7 (3000bp and 10 million bps)
+enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
+promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
+enhancer$cre <- "enhancer"
+promoter$cre <- "promoter"
+enhancer = enhancer[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
+promoter = promoter[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
+
+promoter.subset=subset(promoter, log10_abs_dist >= 3.5)
+promoter = subset(promoter, log10_abs_dist <3.5)
+promoter.subset$cre = "subset"
+data = rbind(promoter, promoter.subset)
+data = data[,log10_intensity:=log10(V7)]
+
+p <- ggplot(data, aes(factor(cre), y = log10_intensity)) + 
+  #geom_violin(aes(fill=factor(cre), color=factor(cre)),
+   # position = "dodge"
+   # )+
+  geom_boxplot(aes(color=factor(cre)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("Peak intensity") 
+pdf("distalH3K4me3PeakIntensity.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" )
+dev.off()
+
+res <- wilcox.test(V7 ~ cre, data = data,
+                   exact = FALSE)
+
+# Number of peaks per gene
+enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
+promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
+enhancer$cre <- "enhancer"
+promoter$cre <- "promoter"
+peak <- rbind(enhancer, promoter)
+peak.counts <- peak %>% as.data.frame() %>% group_by(geneId, cre) %>%
+    summarise(count = n(), .groups = 'drop') %>% as.data.table()
+peak.counts = peak.counts[,log10_abs_dist:=log10(count)][,log10_abs_dist:=ifelse(count<0,-log10_abs_dist,log10_abs_dist)]
+pick <- peak.counts %>% filter(count > 50)
+
+p <- ggplot(peak.counts, aes(factor(cre), y = log10_abs_dist)) + 
+  geom_violin(aes(fill=factor(cre), color=factor(cre))) +
+  geom_boxplot(aes(color=factor(cre)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  )  +
+  geom_text(data = pick, aes(factor(cre), log10_abs_dist), label = pick$geneId, check_overlap = TRUE, size=3, position = position_jitter(w = 0.3)) +
+  theme_bw() + xlab("") + ylab("Log 10 number of peaks") 
+pdf("numberPeaksPerGene.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9"))
+dev.off()
+
+## gene length and number of peaks per gene
+p <- ggplot(peak.counts, aes(x=log10_abs_length, y = log10(n))) + 
+  geom_point()+ theme_bw() + xlab("Gene Length") + ylab("Log 10 number of peaks") 
+pdf("numberPeaksvsGeneLength.pdf", width=9, height = 8)
+p
+dev.off()
+
+
+
+
+
+
+
diff --git a/dunnart/scripts/subsample.sh b/dunnart/scripts/subsample.sh
old mode 100644
new mode 100755
diff --git a/dunnart/scripts/trimfastq.py b/dunnart/scripts/trimfastq.py
old mode 100644
new mode 100755
diff --git a/geneSet_upsetPlot.R b/geneSet_upsetPlot.R
new file mode 100644
index 0000000000000000000000000000000000000000..7d6a787946f6f6c0f1c5cdbf56e7dce908f06907
--- /dev/null
+++ b/geneSet_upsetPlot.R
@@ -0,0 +1,97 @@
+## Laura Cook 2021
+## This script takes separate annotation peak text files, extracts the geneIDs and intersects them
+## The intersection of geneIds between the different samples is then plotted using an upset plot
+
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(ComplexHeatmap)
+library(plyr)
+library(UpSetR)
+
+setwd("../../../cross_species/peakAnnotation/")
+
+geneSetUpset <- function(fileList, plot){
+    files =list.files(fileList, pattern= ".txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(data) = c("P0","E10.5","E11.5","E12.5", "E13.5","E14.5", "E15.5")
+    data$P0$group <- "dunnart"
+    data = lapply(data, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
+    subset = lapply(data, function(x) x %>% dplyr::select(mouseensembl) %>% as.data.table() %>% unique())
+    df1 = Map(mutate, subset, stage = names(subset)) 
+    merged <- df1 %>% purrr::reduce(full_join, by = "mouseensembl")  # join all dataframes by ensembl geneID
+    merged[is.na(merged)] <- 0
+    merged = as.data.frame(merged)
+    colnames(merged) = c("geneId", "P0", "E10.5", "E11.5", "E12.5", "E13.5", "E14.5", "E15.5")
+    geneId = merged$geneId
+    upset.df = data.frame(lapply(merged[,2:8], function(x) as.numeric(x!="0")))
+    rownames(upset.df) = geneId
+
+    pdf(plot)
+    print(upset(upset.df, nsets=7, order.by="freq", number.angles = 45,
+    queries = list(list(
+        query = elements,
+        params = list("genomic_annotation"),
+        color = "#Df5286",
+        query.name = "Genomic annotation"
+    ))))
+    dev.off()
+}
+
+geneSetUpset(fileList = "consensus/promoters/clustered", plot = "promoters_upsetPlot.pdf")
+geneSetUpset(fileList = "consensus/enhancers/annotation", plot = "enhancers_upsetPlot.pdf")
+
+
+## Overlap between enhancers and promoters
+vennDiagrams <- function(enhancers, promoters, plotName){
+    enhancers = fread(enhancers)
+    promoters = fread(promoters)
+    enhancers = enhancers=setnames(enhancers, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table()
+    promoters = promoters=setnames(promoters, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table()
+
+    enhancerIDs = unique(enhancers$mouseensembl)
+    promoterIDs = unique(promoters$mouseensembl)
+    venn.diagram(
+      x = list(enhancerIDs, promoterIDs),
+      category.names = c("enhancers" , "promoters"),
+      filename = plotName,
+      output=TRUE)
+}
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E10.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E10.filtered_annotation.txt", 
+plotName="E10_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E11.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E11.filtered_annotation.txt", 
+plotName="E11_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E12.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E12.filtered_annotation.txt", 
+plotName="E12_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E13.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E13.filtered_annotation.txt", 
+plotName="E13_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E14.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E14.filtered_annotation.txt", 
+plotName="E14_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/E15.5_enhancer_annotation.txt", 
+promoters="consensus/promoters/clustered/clustered.E15.filtered_annotation.txt", 
+plotName="E15_cre_vennDiagram.png")
+
+vennDiagrams(enhancers="consensus/enhancers/annotation/dunnart_enhancer_annotationConvertedIDs.txt", 
+promoters="consensus/promoters/clustered/cluster1_promoters_annotationConvertedIDs.txt", 
+plotName="dunnart_cre_vennDiagram.png")
\ No newline at end of file
diff --git a/genomicRegionsPlot.R b/genomicRegionsPlot.R
new file mode 100644
index 0000000000000000000000000000000000000000..cd887fefed58aa69c1473af342d0b5468bee5d64
--- /dev/null
+++ b/genomicRegionsPlot.R
@@ -0,0 +1,46 @@
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(ComplexHeatmap)
+library(plyr)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation")
+genomicRegions <- function(fileList, annotation, colors){
+    files =list.files(fileList, pattern= ".txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    #data = lapply(data, function(x) x=setnames(x, old="CpG%", new="CpG", skip_absent=TRUE) %>% as.data.table())
+    #data = lapply(data, function(x) x=setnames(x, old="GC%", new="GC", skip_absent=TRUE) %>% as.data.table())
+    names(data) = c("P0","E10.5","E11.5","E12.5", "E13.5","E14.5", "E15.5")
+    subset = lapply(data, function(x) x %>% dplyr::select(annotation) %>% as.data.table())
+    df1 = Map(mutate, subset, stage = names(subset))
+    df1 = rbindlist(df1,) ## bind rows back together
+    df1 = map_df(df1, ~ gsub(" .*", "", .x))# remove everything after the first space
+    df1 = df1[complete.cases(df1), ]
+    tbl = with(df1, table(annotation, stage)) %>% as.data.frame()
+    
+    # Plot proportion of peaks in each genomic region
+    pdf(annotation, width=10, height=6)
+    ggplot(tbl, aes(factor(stage), Freq, fill=annotation)) + 
+    geom_bar(position=position_stack(reverse = FALSE), stat="identity") +
+    #geom_text(data=subset(data,Freq != 0),aes(label=Freq, y=pos), size=3)+
+    scale_fill_manual(values = colors)  + 
+    theme_minimal() + ylab("Number of peaks") +
+    xlab("")
+    dev.off()
+}
+
+genomicRegions(fileList = "consensus/promoters/clustered", annotation = "genomicRegions_promoters.pdf", colors =c('#4d004b','#810f7c','#88419d','#8c6bb1','#8c96c6','#9ebcda','#bfd3e6'))
+genomicRegions(fileList = "consensus/enhancers/annotation", annotation = "genomicRegions_enhancers.pdf", colors =c('#fff7ec','#fee8c8','#fdd49e','#fdbb84','#fc8d59','#ef6548','#d7301f','#b30000','#7f0000'))
+
+
+colors =c('#084081','#0868ac','#2b8cbe','#4eb3d3','#7bccc4','#a8ddb5','#ccebc5')
\ No newline at end of file
diff --git a/go_semantic_similarity.R b/go_semantic_similarity.R
new file mode 100644
index 0000000000000000000000000000000000000000..8a87e017bdd303cc354327f8af78274cec118201
--- /dev/null
+++ b/go_semantic_similarity.R
@@ -0,0 +1,135 @@
+## input: vector of GO terms
+## output: heatmap semantic similarity scores
+library(tools)
+library(GOSemSim)
+library(ComplexHeatmap)
+library(circlize)
+library('BiocParallel')  
+library(data.table)
+library(tidyverse)
+
+## GO data from mouse org database for biological processes
+## Can do for mouse phenotype and molecular processes
+mmGO = godata('org.Mm.eg.db', ont="BP") 
+plot_dir = ("consensus/promoters/")
+## Wang is actually a guy but here it refers to a semantic similarity metric (see https://yulab-smu.top/biomedical-knowledge-mining-book/semantic-similarity-overview.html)
+## combine = 'BMA' returns a single value estimating how similar the 2 sets are, you can change it with min,max,avg ... (see link above)
+
+GOsimilarity <- function(fileList, file_to_compare_to){
+    files =list.files(fileList, pattern= "", full.names=T) # create list of files in directory
+    files = as.list(files)
+    list = lapply(files, function(x) read.table(x, header=TRUE, sep="\t", quote = "") %>% as.data.table()) # read in all files
+    ## this keeps only the significant GO terms
+    significant_go_terms=lapply(list,function(x) x=x[p.adjust<=0.01])
+    listVector = lapply(significant_go_terms, function(x) dplyr::pull(x, ID))
+    file_input = read.table(file_to_compare_to, sep="\t", header=TRUE, quote="") %>% as.data.frame()
+    file_sig_go = dplyr::pull(file_input[file_input$p.adjust <= 0.01,], ID)
+
+    ## This extracts the file names for all the files in the list (all embryonic stages)
+    file_names=as.character(list.files(fileList,recursive =F,full.names = F)) ## directory with all embryonic stages
+    file_names=gsub("\\..*","",file_names) ## Removes everything except 'E*.5'
+    file_names=gsub("[^0-9]","", file_names) ## Removes the 'E' so that it's a number
+    names(listVector)=file_names ## Add new names to list
+
+    ## Check if input file name is less than or equal to any in listVector
+    ## This will avoid running comparisons that have already been run
+    ## Eg. E10.5 vs E11.5 is the same as E11.5 vs E10.5
+    file_to_compare_to_name = basename(file_path_sans_ext(file_to_compare_to)) ## Name of file to compare to
+    if(grepl('^E', file_to_compare_to_name) == TRUE){ ## If file begins with E (this is so that it doesn't stall with the dunnart files)
+        file_to_compare_to_name=gsub("\\..*","",file_to_compare_to_name) ## Removes everything except 'E*.5'
+        file_to_compare_to_name=gsub("[^0-9]","",file_to_compare_to_name) ## Removes the 'E' so that it's a number
+        listVectorSubset = listVector[names(listVector) > file_to_compare_to_name] ## Keep only vectors in the list that are greater than the file being compared to
+                                                                                ## Eg. this means that when comparing E12 to the list then it will only compare to 13,14 & 15
+    } else 
+    listVectorSubset = listVector
+
+    ## Compare the similarity between GO lists
+    ## This will parallelise the lapply function 
+    comparisons = bplapply(listVectorSubset,function(x) mgoSim(file_sig_go,x, semData=mmGO,measure="Wang", combine='BMA')) 
+    return(comparisons)
+}
+
+## This command checks the time it takes to run
+## One comparison is about 95 seconds
+system.time(mgoSim(listVector[[1]], listVector[[2]],semData=mmGO, measure="Wang", combine='BMA'))
+
+register(SerialParam())
+
+
+## Promoters
+plot_dir = ("consensus/promoters/")
+
+dunnartvsmouse = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/dunnart_GO/promoter_mm10GOenrich") #dunnart versus all mouse stages
+
+e10vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E10.5_promoter_mm10GOenrich") 
+
+e11vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E11.5_promoter_mm10GOenrich")
+
+e12vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E12.5_promoter_mm10GOenrich") 
+
+e13vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E13.5_promoter_mm10GOenrich") 
+
+e14vs = GOsimilarity(fileList = "consensus/promoters/mouse_GO", 
+file_to_compare_to = "consensus/promoters/mouse_GO/E14.5_promoter_mm10GOenrich") 
+
+
+## Enhancers
+plot_dir = ("consensus/enhancers/")
+
+dunnartvsmouse = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/dunnart_GO/enhancer_mm10GOenrich_subsample.txt")
+
+e10vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E10.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e11vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E11.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e12vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E12.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e13vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E13.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+e14vs = GOsimilarity(fileList = "consensus/enhancers/mouse_GO", 
+file_to_compare_to = "consensus/enhancers/mouse_GO/E14.5_enhancer_mm10GOenrich") #dunnart versus all mouse stages
+
+
+vector_of_scores <- c(
+    dunnartvsmouse$'10', dunnartvsmouse$'11', dunnartvsmouse$'12', dunnartvsmouse$'13', dunnartvsmouse$'14',dunnartvsmouse$'15',
+    e10vs$'11',e10vs$'12',e10vs$'13',e10vs$'14',e10vs$'15',
+    e11vs$'12',e11vs$'13',e11vs$'14',e11vs$'15',
+    e12vs$'13',e12vs$'14',e12vs$'15',
+    e13vs$'14',e13vs$'15',
+    e14vs$'15'
+
+)
+my_matrix <- matrix(0,7,7) ## creates a n x n square 0 matrix
+
+rownames(my_matrix) = c('Dunnart','E10','E11','E12','E13','E14','E15')
+colnames(my_matrix) = c('Dunnart','E10','E11','E12','E13','E14','E15')
+
+my_matrix[ col(my_matrix) < row(my_matrix) ] <- vector_of_scores
+my_matrix <- my_matrix + t(my_matrix)
+diag(my_matrix) <- 1
+my_matrix
+
+## here i am just plotting a basic heatmap
+## to customise it see https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors  
+## in particular you'll have to change the colors to something different to a diverging palette (try viridis/inferno...)
+## but there are many more customisation worth checking
+pdf(paste(plot_dir,'enhancer_GO_semantic_scores.pdf',sep=''),width=10,height = 7)
+Heatmap(my_matrix, name = "score", col=c('#0868ac', '#43a2ca','#7bccc4', '#bae4bc','#f0f9e8'))
+dev.off()
+
+pdf(paste(plot_dir,'promoter_GO_semantic_scores.pdf',sep=''),width=10,height = 7)
+Heatmap(my_matrix, name = "score", col=c('#810f7c', '#8856a7','#8c96c6', '#b3cde3','#edf8fb'), heatmap_legend_param = list(
+        title = "GO similarity score", at = c(0.7,0.8,0.9, 1)))
+dev.off()
+
+
diff --git a/liftOverPeaks.R b/liftOverPeaks.R
new file mode 100644
index 0000000000000000000000000000000000000000..ec641f7ae1892b7e60998e6be8b950ca5a03feb2
--- /dev/null
+++ b/liftOverPeaks.R
@@ -0,0 +1,34 @@
+
+## Import packages
+library(data.table)
+library(ggplot2)
+
+## Create datatable with peak overlap counts
+
+promoters = data.table(
+    samples = c("E10.5","E11.5","E12.5","E13.5","E14.5","E15.5"),
+    liftOver.total = rep(as.numeric(1881), 6),
+    common.peaks = c(923,882,892,854,907,821)
+  )
+
+enhancers = data.table(
+    samples = c("E10.5","E11.5","E12.5","E13.5","E14.5","E15.5"),
+    liftOver.total = rep(as.numeric(3820), 6),
+    common.peaks = c(123, 72, 104, 161, 122, 66)
+)
+
+## Combine into a list
+list = list(promoters, enhancers)
+list[[1]]$cre = "promoters"
+list[[2]]$cre = "enhancers"
+
+## Add peaks active at each stage as a fraction of all peaks that can be reciprocally lifted over
+list = lapply(list, function(x) transform(x, peakFraction = (common.peaks / liftOver.total)*100))
+
+## Merge datatables and then plot peak fractions
+df = rbindlist(list, )
+pdf("liftOver_peaks.pdf", width=10, height=6)
+ggplot(df, aes(fill=cre, y=peakFraction, x=samples)) + 
+    geom_bar(position="dodge", stat="identity")
+dev.off()
+
diff --git a/mouse/README.md b/mouse/README.md
old mode 100644
new mode 100755
index c96c13ed462296e363a59d4e3bb1e1eeb5bb4023..e71fa9d3952edfd9d7b8cd4674f3671d2610074a
--- a/mouse/README.md
+++ b/mouse/README.md
@@ -246,21 +246,55 @@ therefore if amount that overlaps between each replicate divided by the length o
 Find uniquely H3K4me3 sites (i.e. peaks that don't overlap with H3K27ac):
 
 ```
-bedtools intersect -v -a H3K4me3_overlap.narrowPeak -b H3K27ac_overlap.narrowPeak > H3K4me3_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E10.5_overlap.narrowPeak -b H3K27ac_E10.5_overlap.narrowPeak > H3K4me3_E10.5_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E11.5_overlap.narrowPeak -b H3K27ac_E11.5_overlap.narrowPeak > H3K4me3_E11.5_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E12.5_overlap.narrowPeak -b H3K27ac_E12.5_overlap.narrowPeak > H3K4me3_E12.5_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E13.5_overlap.narrowPeak -b H3K27ac_E13.5_overlap.narrowPeak > H3K4me3_E13.5_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E14.5_overlap.narrowPeak -b H3K27ac_E14.5_overlap.narrowPeak > H3K4me3_E14.5_only.narrowPeak
+bedtools intersect -v -a H3K4me3_E15.5_overlap.narrowPeak -b H3K27ac_E15.5_overlap.narrowPeak > H3K4me3_E15.5_only.narrowPeak
+
 ```
 
 Find unique H3K27ac sites (i.e. peaks that don't overlap with H3K4me3):
 ```
-bedtools intersect -v -a H3K27ac_overlap.narrowPeak -b H3K4me3_overlap.narrowPeak > H3K27ac_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E10.5_overlap.narrowPeak -b H3K4me3_E10.5_overlap.narrowPeak > H3K27ac_E10.5_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E11.5_overlap.narrowPeak -b H3K4me3_E11.5_overlap.narrowPeak > H3K27ac_E11.5_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E12.5_overlap.narrowPeak -b H3K4me3_E12.5_overlap.narrowPeak > H3K27ac_E12.5_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E13.5_overlap.narrowPeak -b H3K4me3_E13.5_overlap.narrowPeak > H3K27ac_E13.5_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E14.5_overlap.narrowPeak -b H3K4me3_E14.5_overlap.narrowPeak > H3K27ac_E14.5_only.narrowPeak
+bedtools intersect -v -a H3K27ac_E15.5_overlap.narrowPeak -b H3K4me3_E15.5_overlap.narrowPeak > H3K27ac_E15.5_only.narrowPeak
 ```
 
 Find peaks common between H3K27ac & H3K4me3 with a reciprocal overlap of at least 50%.
 ```
-bedtools intersect -f 0.5 -r -a H3K4me3_only.narrowPeak -b H3K27ac_only.narrowPeak > H3K4me3_and_H3K27ac.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E10.5_overlap.narrowPeak -b H3K27ac_E10.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E10.5.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E11.5_overlap.narrowPeak -b H3K27ac_E11.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E11.5.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E12.5_overlap.narrowPeak -b H3K27ac_E12.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E12.5.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E13.5_overlap.narrowPeak -b H3K27ac_E13.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E13.5.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E14.5_overlap.narrowPeak -b H3K27ac_E14.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E14.5.narrowPeak
+bedtools intersect -f 0.5 -r -a H3K4me3_E15.5_overlap.narrowPeak -b H3K27ac_E15.5_overlap.narrowPeak > H3K4me3_and_H3K27ac_E15.5.narrowPeak
+```
+
+Combine peak files
+
 ```
+cat H3K4me3_E10.5_only.narrowPeak H3K4me3_and_H3K27ac_E10.5.narrowPeak | sort -k 4,4 > E10.5_promoter_peaks.narrowPeak
+cat H3K4me3_E11.5_only.narrowPeak H3K4me3_and_H3K27ac_E11.5.narrowPeak | sort -k 4,4 > E11.5_promoter_peaks.narrowPeak
+cat H3K4me3_E12.5_only.narrowPeak H3K4me3_and_H3K27ac_E12.5.narrowPeak | sort -k 4,4 > E12.5_promoter_peaks.narrowPeak
+cat H3K4me3_E13.5_only.narrowPeak H3K4me3_and_H3K27ac_E13.5.narrowPeak | sort -k 4,4 > E13.5_promoter_peaks.narrowPeak
+cat H3K4me3_E14.5_only.narrowPeak H3K4me3_and_H3K27ac_E14.5.narrowPeak | sort -k 4,4 > E14.5_promoter_peaks.narrowPeak
+cat H3K4me3_E15.5_only.narrowPeak H3K4me3_and_H3K27ac_E15.5.narrowPeak | sort -k 4,4 > E15.5_promoter_peaks.narrowPeak
+
 
+cat H3K27ac_E10.5_only.narrowPeak | sort -k 4,4 > E10.5_enhancer_peaks.narrowPeak
+cat H3K27ac_E11.5_only.narrowPeak | sort -k 4,4 > E11.5_enhancer_peaks.narrowPeak
+cat H3K27ac_E12.5_only.narrowPeak | sort -k 4,4 > E12.5_enhancer_peaks.narrowPeak
+cat H3K27ac_E13.5_only.narrowPeak | sort -k 4,4 > E13.5_enhancer_peaks.narrowPeak
+cat H3K27ac_E14.5_only.narrowPeak | sort -k 4,4 > E14.5_enhancer_peaks.narrowPeak
+cat H3K27ac_E15.5_only.narrowPeak | sort -k 4,4 > E15.5_enhancer_peaks.narrowPeak
 
 
+```
 
 
 
diff --git a/mouse/Snakefile1_H3K4me3 b/mouse/Snakefile1_H3K4me3
new file mode 100755
index 0000000000000000000000000000000000000000..c06f81611628852f070c327e3730bc410c41fe4f
--- /dev/null
+++ b/mouse/Snakefile1_H3K4me3
@@ -0,0 +1,717 @@
+## Author: Laura E Cook, University of Melbourne
+## Purpose: ChIP-seq pipeline for histone marks
+## This workflow only works for single-end reads
+    # 1. FastQC on raw reads
+    # 2. Alignment (downloaded from ENCODE)
+    # 3. Filtering
+    # 4. Alignment QC & Library Complexity
+    # 5. deepTools
+    # 7. cross correlation (SPP)
+    # 8. Call narrow peaks (MACS2)
+    # 9. Create consensus peaksets
+    # 10. Present QC for raw read, alignment, peak-calling in MultiQC
+
+
+configfile: "configs/config_H3K4me3.yaml"
+# Contains sample and genome information
+# calls the SRR.txt file that contains the samples as either IP or control
+
+import csv
+import os
+
+IPS = []
+INPUTS = []
+MARK = []
+STAGE = []
+
+with open(config['SAMPLES'], "r") as f:
+    reader = csv.reader(f, delimiter = "\t")
+    # skip the header
+    header = next(reader)
+    for row in reader:
+        STAGE.append(row[2])
+        MARK.append(row[1])
+        IPS.append(row[3])
+        INPUTS.append(row[4])
+
+## multiple samples may use the same control input files
+unique_inputs = list(set(INPUTS))
+unique_marks = list(set(MARK))
+unique_stages = list(set(STAGE))
+
+## combine all samples
+all_samples = IPS + INPUTS
+
+# ===============================================================================================
+#        Output targets
+# ===============================================================================================
+
+rule all:
+    input:
+    # Alignment and Filtering in results folder
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.q30.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+    # Merging the BAM replicates for the subsampled dedup BAM files
+        "results_10M/bwa/E10.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/E11.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/E12.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/E13.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/E14.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/E15.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        "results_10M/bwa/input_E10.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/bwa/input_E11.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/bwa/input_E12.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/bwa/input_E13.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/bwa/input_E14.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/bwa/input_E15.5_H3K4me3_q30.sorted.dedup.bam",
+        "results_10M/logs/E10.5_H3K4me3.mergeBAM",
+        "results_10M/logs/E11.5_H3K4me3.mergeBAM",
+        "results_10M/logs/E12.5_H3K4me3.mergeBAM",
+        "results_10M/logs/E13.5_H3K4me3.mergeBAM",
+        "results_10M/logs/E14.5_H3K4me3.mergeBAM",
+        "results_10M/logs/E15.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E10.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E11.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E12.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E13.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E14.5_H3K4me3.mergeBAM",
+        "results_10M/logs/input_E15.5_H3K4me3.mergeBAM",
+    # Quality controls for deepTools
+        "results_10M/qc/H3K4me3_multibamsum.npz",
+        "results_10M/qc/H3K4me3_multibamsum.tab",
+        "results_10M/qc/H3K4me3_pearsoncor_multibamsum.pdf",
+        "results_10M/qc/H3K4me3_pearsoncor_multibamsum_matrix.txt",
+        expand("results_10M/qc/{sample}_{stage}_{mark}.SeqDepthNorm.bw", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        "results_10M/qc/H3K4me3_multiBAM_fingerprint.pdf",
+        "results_10M/qc/H3K4me3_multiBAM_fingerprint_metrics.txt",
+        "results_10M/qc/H3K4me3_multiBAM_fingerprint_rawcounts.txt",
+        "results_10M/qc/H3K4me3_plot_coverage.pdf",
+        "results_10M/qc/H3K4me3_plot_coverage_rawcounts.tab",
+    # Quality control non-redundant fraction etc.
+        expand("results/logs/{sample}_{stage}_{mark}.pbc.sort", zip, sample=INPUTS, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.pbc.qc", zip, sample=INPUTS, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf", zip, sample=all_samples, stage=STAGE, mark=MARK),
+    # MACS2 peak calling    
+        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.xls", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_summits.bed", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        expand("results_10M/logs/{case}_vs_{control}_{stage}_{mark}_call_peaks_macs2.log", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        expand("results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bed", zip, case=IPS, stage=STAGE, mark=MARK),
+        expand("results_10M/logs/{case}_{stage}_{mark}.bamToBed", zip, case=IPS, stage=STAGE, mark=MARK),
+        expand("results_10M/qc/{case}_vs_{control}_{stage}_{mark}.frip.txt", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        directory("results_10M/macs2/pooled/"),
+        "results_10M/macs2/H3K4me3_E10.5_overlap.narrowPeak",
+        "results_10M/macs2/H3K4me3_E11.5_overlap.narrowPeak",
+        "results_10M/macs2/H3K4me3_E12.5_overlap.narrowPeak",
+        "results_10M/macs2/H3K4me3_E13.5_overlap.narrowPeak",
+        "results_10M/macs2/H3K4me3_E14.5_overlap.narrowPeak",
+        "results_10M/macs2/H3K4me3_E15.5_overlap.narrowPeak",
+        "results_10M/qc/H3K4me3_E10.5_overlap.frip",
+        "results_10M/qc/H3K4me3_E11.5_overlap.frip",
+        "results_10M/qc/H3K4me3_E12.5_overlap.frip",
+        "results_10M/qc/H3K4me3_E13.5_overlap.frip",
+        "results_10M/qc/H3K4me3_E14.5_overlap.frip",
+        "results_10M/qc/H3K4me3_E15.5_overlap.frip",
+        # directory("results_10M/multiqc/multiqc_report_data/"),
+        # "results_10M/multiqc/multiqc_report.html"
+
+# ===============================================================================================
+#  1. FASTQC
+# ===============================================================================================
+
+## Done separately because fastq and BAM files don't share file prefixes so it becomes very messy
+# without renaming everything
+
+# ===============================================================================================
+#  2. ALIGNMENT
+#   > ENCODE alignment downloaded from ENCODE database
+# ===============================================================================================
+
+## Singled-end 36-50nt reads
+## bwa-aln used for aligning reads to the mouse genome (mm10 assembly)
+## Alignment command: bwa-aln -q 5 -l 32 -k 2 <reference_file> <read_file>
+## bwa version version 0.7.7
+
+## -q = parameter for read trimming
+## -l = read length
+## -k = Maximum edit distance in the seed
+
+# ===============================================================================================
+#  4. FILTERING
+#   > remove unmapped, mate unmapped
+#   > remove low MAPQ reads (-q 30)
+#   > -F 1804
+#           -Read unmapped
+#           -Mate unmapped
+#           -Not primary alignment
+#           -Read fails platform/vendor quality checks
+#           -Read is PCR or optical duplicate
+#   > mark duplicates & remove duplicates (picard)
+#   > re-filter, sort and index final BAM
+# ===============================================================================================
+
+rule filter:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}.bam"
+    output:
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
+    log:
+        "results/logs/{sample}_{stage}_{mark}.filter"
+    shell:
+        "samtools view -b -F 1804 -q 30 {input} | samtools sort -o {output} - 2> {log}"
+
+rule markDups:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
+    output:
+        bam="results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
+        dupQC="results/bwa/{sample}_{stage}_{mark}.dupmark.qc"
+    log:
+        "results/logs/{sample}_{stage}_{mark}.dedup"
+    shell:
+        "picard MarkDuplicates I={input} O={output.bam} \
+        METRICS_FILE={output.dupQC} REMOVE_DUPLICATES=false ASSUME_SORTED=true 2> {log}"
+
+rule dedup:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+    output:
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    log:
+        "results/logs/{sample}_{stage}_{mark}.dedup"
+    shell:
+        "samtools view -F 1804 -b {input} | samtools sort -o {output} - 2> {log}"
+
+rule normalise10Mreads:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    output:
+        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    shell:
+        """
+        frac=$( samtools idxstats {input} | cut -f3 | awk 'BEGIN {total=0} {total += $1} END {frac=10000000/total; if (frac > 1) {print 1} else {print frac}}' )\
+        samtools view -bs $frac {input} > {output}
+        """
+
+rule indexBam:
+    input:
+        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    output:
+        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"
+    log:
+        "results_10M/logs/{sample}_{stage}_{mark}.indexBam"
+    shell:
+        "samtools index {input} {output} 2> {log}"
+
+rule mergeBAMreplicates:
+    input:
+        E10 = ["results_10M/bwa/ENCFF124UYX_E10.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF045IPK_E10.5_H3K4me3_q30.sorted.dedup.bam"],
+        E11 = ["results_10M/bwa/ENCFF760QYZ_E11.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF717QDV_E11.5_H3K4me3_q30.sorted.dedup.bam"],
+        E12 = ["results_10M/bwa/ENCFF182ZPF_E12.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF941QJZ_E12.5_H3K4me3_q30.sorted.dedup.bam"],
+        E13 = ["results_10M/bwa/ENCFF485UDC_E13.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF124TAB_E13.5_H3K4me3_q30.sorted.dedup.bam"],
+        E14 = ["results_10M/bwa/ENCFF724DMU_E14.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF665QBJ_E14.5_H3K4me3_q30.sorted.dedup.bam"],
+        E15 = ["results_10M/bwa/ENCFF258KCR_E15.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF401BKM_E15.5_H3K4me3_q30.sorted.dedup.bam"],
+        E10C = ["results_10M/bwa/ENCFF157KEH_E10.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF825AVI_E10.5_H3K4me3_q30.sorted.dedup.bam"],
+        E11C = ["results_10M/bwa/ENCFF184CUE_E11.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF376FGM_E11.5_H3K4me3_q30.sorted.dedup.bam"],
+        E12C = ["results_10M/bwa/ENCFF203JQV_E12.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF058AUT_E12.5_H3K4me3_q30.sorted.dedup.bam"],
+        E13C = ["results_10M/bwa/ENCFF117QRC_E13.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF248PGK_E13.5_H3K4me3_q30.sorted.dedup.bam"],
+        E14C = ["results_10M/bwa/ENCFF784ORI_E14.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF002HZV_E14.5_H3K4me3_q30.sorted.dedup.bam"],
+        E15C = ["results_10M/bwa/ENCFF727QTS_E15.5_H3K4me3_q30.sorted.dedup.bam", "results_10M/bwa/ENCFF182XFG_E15.5_H3K4me3_q30.sorted.dedup.bam"],
+    output:
+        E10 = "results_10M/bwa/E10.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E11 = "results_10M/bwa/E11.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E12 = "results_10M/bwa/E12.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E13 = "results_10M/bwa/E13.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E14 = "results_10M/bwa/E14.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E15 = "results_10M/bwa/E15.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E10C = "results_10M/bwa/input_E10.5_H3K4me3_q30.sorted.dedup.bam",
+        E11C = "results_10M/bwa/input_E11.5_H3K4me3_q30.sorted.dedup.bam",
+        E12C = "results_10M/bwa/input_E12.5_H3K4me3_q30.sorted.dedup.bam",
+        E13C = "results_10M/bwa/input_E13.5_H3K4me3_q30.sorted.dedup.bam",
+        E14C = "results_10M/bwa/input_E14.5_H3K4me3_q30.sorted.dedup.bam",
+        E15C = "results_10M/bwa/input_E15.5_H3K4me3_q30.sorted.dedup.bam"
+    log:
+        E10 = "results_10M/logs/E10.5_H3K4me3.mergeBAM",
+        E11 = "results_10M/logs/E11.5_H3K4me3.mergeBAM",
+        E12 = "results_10M/logs/E12.5_H3K4me3.mergeBAM",
+        E13 = "results_10M/logs/E13.5_H3K4me3.mergeBAM",
+        E14 = "results_10M/logs/E14.5_H3K4me3.mergeBAM",
+        E15 = "results_10M/logs/E15.5_H3K4me3.mergeBAM",
+        E10C = "results_10M/logs/input_E10.5_H3K4me3.mergeBAM",
+        E11C = "results_10M/logs/input_E11.5_H3K4me3.mergeBAM",
+        E12C = "results_10M/logs/input_E12.5_H3K4me3.mergeBAM",
+        E13C = "results_10M/logs/input_E13.5_H3K4me3.mergeBAM",
+        E14C = "results_10M/logs/input_E14.5_H3K4me3.mergeBAM",
+        E15C = "results_10M/logs/input_E15.5_H3K4me3.mergeBAM"
+    run:
+        shell("samtools merge {output.E10} {input.E10} 2> {log.E10}")
+        shell("samtools merge {output.E11} {input.E11} 2> {log.E11}")
+        shell("samtools merge {output.E12} {input.E12} 2> {log.E12}")
+        shell("samtools merge {output.E13} {input.E13} 2> {log.E13}")
+        shell("samtools merge {output.E14} {input.E14} 2> {log.E14}")
+        shell("samtools merge {output.E15} {input.E15} 2> {log.E15}")
+        shell("samtools merge {output.E10C} {input.E10C} 2> {log.E10C}")
+        shell("samtools merge {output.E11C} {input.E11C} 2> {log.E11C}")
+        shell("samtools merge {output.E12C} {input.E12C} 2> {log.E12C}")
+        shell("samtools merge {output.E13C} {input.E13C} 2> {log.E13C}")
+        shell("samtools merge {output.E14C} {input.E14C} 2> {log.E14C}")
+        shell("samtools merge {output.E15C} {input.E15C} 2> {log.E15C}")
+
+
+# ===============================================================================================
+#  4. ALIGNMENT QC
+#   > SAMtools flagstat statistics
+#   > CollectMultipleMetrics (picard)
+#   > Compute Library Complexity (PreSeq)
+# ===============================================================================================
+
+rule mappingStats:
+    input:
+        a="results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam",
+        b="results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
+        c="results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam",
+        d="results/bwa/{sample}_{stage}_{mark}.bam"
+    output:
+        a="results/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc",
+        b="results/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc",
+        c="results/qc/{sample}_{stage}_{mark}.q30.flagstat.qc",
+        d="results/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc",
+    run:
+        shell("samtools flagstat {input.a} > {output.a}")
+        shell("samtools flagstat {input.b} > {output.b}")
+        shell("samtools flagstat {input.c} > {output.c}")
+        shell("samtools flagstat {input.d} > {output.d}")
+
+
+rule sort_name:
+     input:
+         "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+     output:
+         tmp = "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
+     log:
+         "results/logs/{sample}_{stage}_{mark}.pbc.sort"
+     shell:
+         "samtools sort -n {input} -o {output.tmp} 2> {log}"
+
+rule estimate_lib_complexity:
+     input:
+         "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
+     output:
+         qc = "results/qc/{sample}_{stage}_{mark}.pbc.qc"
+     log:
+         "results/logs/{sample}_{stage}_{mark}.pbc"
+     shell:
+        """
+        bedtools bamtobed -i {input} | grep -v 'chrM' \
+        | awk 'BEGIN{{OFS="\\t"}}{{print $1,$2,$3,$6}}' \
+        | sort | uniq -c \
+        | awk 'BEGIN{{mt=0;m0=0;m1=0;m2=0}} ($1==1){{m1=m1+1}} ($1==2){{m2=m2+1}} \
+        {{m0=m0+1}} {{mt=mt+$1}} END{{printf "%d\\t%d\\t%d\\t%d\\t%f\\t%f\\t%f\\n" ,mt,m0,m1,m2,m0/mt,m1/m0,m1/m2}}' \
+        > {output.qc}
+        """
+
+
+## convert to bed
+## print read 1 scaffold, read 1 start coordinate, read 2 scaffold, read 2 end coordinate, strand read 1, strand read 2
+## remove mitochondrial genome
+## sort by position and obtain uniq reads
+## Format of file
+## TotalReadPairs [tab] DistinctReadPairs [tab] OneReadPair [tab] TwoReadPairs [tab] NRF=Distinct/Total [tab] PBC1=OnePair/Distinct [tab] PBC2=OnePair/TwoPair
+
+
+# ===============================================================================================
+#  5. deepTools
+#   > multiBAMsummary
+#   > plotCorrelation
+#   > plotFingerprint
+#   > bamCoverage (read depth normalised bigWig files)
+# ===============================================================================================
+
+rule deeptools_summary:
+    input:
+        expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"], zip, sample=all_samples, mark=MARK, stage=STAGE)
+    output:
+        sum="results_10M/qc/H3K4me3_multibamsum.npz",
+        counts="results_10M/qc/H3K4me3_multibamsum.tab"
+    threads: 32
+    log:
+        "results_10M/logs/H3K4me3_multisummary.deepTools"
+    shell:
+        " multiBamSummary bins \
+        -p {threads} \
+        -b {input} \
+        --centerReads \
+        -out {output.sum} \
+        --outRawCounts {output.counts} 2> {log}"
+
+rule deeptools_correlation:
+    input: "results_10M/qc/H3K4me3_multibamsum.npz"
+    output:
+        fig="results_10M/qc/H3K4me3_pearsoncor_multibamsum.pdf",
+        matrix="results_10M/qc/H3K4me3_pearsoncor_multibamsum_matrix.txt"
+    log:
+        "results_10M/logs/H3K4me3_correlation.deepTools"
+    shell:
+        "plotCorrelation \
+        --corData {input} \
+        --plotFile {output.fig} \
+        --outFileCorMatrix {output.matrix} \
+        --corMethod pearson \
+        --whatToPlot heatmap \
+        --skipZeros \
+        --plotNumbers \
+        --colorMap RdYlBu 2> {log}"
+
+rule deeptools_coverage:
+    input:
+        bam = "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam",
+        bai = "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"
+    output:
+        "results_10M/qc/{sample}_{stage}_{mark}.SeqDepthNorm.bw"
+    log:
+        "results_10M/logs/{sample}_{stage}_{mark}_coverage.deepTools"
+    shell:
+        "bamCoverage --bam {input.bam} --binSize 10 --normalizeUsing RPGC --effectiveGenomeSize 2308125349 --numberOfProcessors 4 -o {output} 2> {log}"
+
+rule deeptools_fingerprint:
+    input:
+        bam = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"], zip, sample=all_samples, stage=STAGE, mark = MARK),
+        bai = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"], zip, sample=all_samples, stage=STAGE, mark = MARK)
+    output:
+        fig="results_10M/qc/H3K4me3_multiBAM_fingerprint.pdf",
+        metrics="results_10M/qc/H3K4me3_multiBAM_fingerprint_metrics.txt",
+        rawcounts="results_10M/qc/H3K4me3_multiBAM_fingerprint_rawcounts.txt"
+    threads: 32
+    log:
+        "results_10M/logs/fingerprint.deepTools"
+    shell:
+        "plotFingerprint -p {threads} \
+        -b {input.bam} \
+        --plotFile {output.fig} \
+        --outQualityMetrics {output.metrics} \
+        --outRawCounts {output.rawcounts} \
+        --minMappingQuality 20 \
+        --skipZeros \
+        --centerReads 2> {log}"
+
+rule deeptools_plotCoverage:
+    input:
+        bam = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"], zip, sample=all_samples, stage=STAGE, mark=MARK),
+        bai = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"], zip, sample=all_samples, stage=STAGE, mark=MARK)
+    output:
+        fig="results_10M/qc/H3K4me3_plot_coverage.pdf",
+        rawcounts="results_10M/qc/H3K4me3_plot_coverage_rawcounts.tab"
+    log:
+        "results_10M/logs/H3K4me3_plotCoverage.deepTools"
+    shell:
+        "plotCoverage --bamfiles {input.bam} --plotFile {output.fig} \
+        -n 1000000 \
+        --outRawCounts {output.rawcounts} \
+        --ignoreDuplicates 2> {log}"
+
+
+# ===============================================================================================
+#  6. Cross-correlation scores - following ENCODE code
+# ===============================================================================================
+
+rule bamtobed_crossC:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+    output:
+        tagAlign = "results/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
+    shell:
+        """
+        bedtools bamtobed -i {input} | \
+        awk 'BEGIN{{OFS="\\t"}}{{$4='N';$5='1000';print $0}}' |\
+        gzip -nc > {output.tagAlign}
+        """
+
+## Subsample 15 million reads for cross-correlation analysis
+## Estimate read length from first 100 reads
+rule subsample_aligned_reads:
+    input:
+        "results/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
+    output:
+        subsample = "results/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz",
+        tmp = "results/bed/{sample}_{stage}_{mark}_R1_trimmed_q30_SE.tagAlign.tmp"
+    params:
+        nreads= 15000000
+    run:
+        shell("""zcat {input} | shuf -n {params.nreads} --random-source=<(openssl enc -aes-256-ctr -pass pass:$(zcat -f {input} | wc -c) -nosalt </dev/zero 2>/dev/null) | gzip -nc > {output.subsample}""")
+        shell("zcat {input} > {output.tmp}")
+
+# Determine exclusion range for fragment length estimation.
+## From bamPEFragmentSize (deepTools) average fragment length is ~220bp
+## See bamPEFragmentSize.histogram.pdf
+# Use a fixed lowerbound at -500.
+# Upperbound E
+#EXCLUSION_RANGE_MAX is
+#   Histone ChIP-seq:  max(read_len + 10, 100)
+# lowerbound is fixed at 500 for both
+
+
+rule cross_correlation_SSP:
+    input:
+        "results/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz"
+    output:
+        CC_SCORES_FILE="results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",
+        CC_PLOT_FILE="results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf"
+    log:
+        "results/logs/{sample}_{stage}_{mark}_filt_15Mreads.SE.spp.log"
+    params:
+        EXCLUSION_RANGE_MIN=-500,
+        EXCLUSION_RANGE_MAX=85
+    shell:
+        "Rscript scripts/run_spp.R -c={input} -savp={output.CC_PLOT_FILE} -out={output.CC_SCORES_FILE} -x={params.EXCLUSION_RANGE_MIN}:{params.EXCLUSION_RANGE_MAX} 2> {log}"
+
+
+# CC_SCORE FILE format
+# Filename <tab> numReads <tab> estFragLen <tab> corr_estFragLen <tab> PhantomPeak <tab> corr_phantomPeak <tab> argmin_corr <tab> min_corr <tab> phantomPeakCoef <tab> relPhantomPeakCoef <tab> QualityTag
+
+
+# ===============================================================================================
+#  7. Call peaks (MACS2)
+# ===============================================================================================
+
+rule call_peaks_macs2:
+    input:
+        control = "results_10M/bwa/{control}_{stage}_{mark}_q30.sorted.dedup.bam",
+        case = "results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bam"
+    output:
+        "results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.xls",
+        "results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_summits.bed",
+        "results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.narrowPeak",
+    log:
+        "results_10M/logs/{case}_vs_{control}_{stage}_{mark}_call_peaks_macs2.log"
+    params:
+        name = "{case}_vs_{control}_{stage}_{mark}_macs2",
+    shell:
+        " macs2 callpeak -f BAM -t {input.case} \
+        -c {input.control} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.name} \
+        -g mm 2> {log} "
+
+rule call_peaks_macs2_pooled_replicates:
+    input:
+        E10 = "results_10M/bwa/E10.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E11 = "results_10M/bwa/E11.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E12 = "results_10M/bwa/E12.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E13 = "results_10M/bwa/E13.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E14 = "results_10M/bwa/E14.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E15 = "results_10M/bwa/E15.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+        E10C = "results_10M/bwa/input_E10.5_H3K4me3_q30.sorted.dedup.bam",
+        E11C = "results_10M/bwa/input_E11.5_H3K4me3_q30.sorted.dedup.bam",
+        E12C = "results_10M/bwa/input_E12.5_H3K4me3_q30.sorted.dedup.bam",
+        E13C = "results_10M/bwa/input_E13.5_H3K4me3_q30.sorted.dedup.bam",
+        E14C = "results_10M/bwa/input_E14.5_H3K4me3_q30.sorted.dedup.bam",
+        E15C = "results_10M/bwa/input_E15.5_H3K4me3_q30.sorted.dedup.bam"
+    output:
+        directory("results_10M/macs2/pooled/")
+    log:
+        E10 = "results_10M/qc/E10.5_H3K4me3.pooled.macs2",
+        E11 = "results_10M/qc/E11.5_H3K4me3.pooled.macs2",
+        E12 = "results_10M/qc/E12.5_H3K4me3.pooled.macs2",
+        E13 = "results_10M/qc/E13.5_H3K4me3.pooled.macs2",
+        E14 = "results_10M/qc/E14.5_H3K4me3.pooled.macs2",
+        E15 = "results_10M/qc/E15.5_H3K4me3.pooled.macs2"
+    params:
+        E10 = "E10.5_H3K4me3.pooled.macs2",
+        E11 = "E11.5_H3K4me3.pooled.macs2",
+        E12 = "E12.5_H3K4me3.pooled.macs2",
+        E13 = "E13.5_H3K4me3.pooled.macs2",
+        E14 = "E14.5_H3K4me3.pooled.macs2",
+        E15 = "E15.5_H3K4me3.pooled.macs2"
+    run:
+        shell("macs2 callpeak -f BAM -t {input.E10} \
+        -c {input.E10C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E10} \
+        -g mm 2> {log.E10}" )
+        shell("macs2 callpeak -f BAM -t {input.E11} \
+        -c {input.E11C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E11} \
+        -g mm 2> {log.E11}" )
+        shell("macs2 callpeak -f BAM -t {input.E12} \
+        -c {input.E12C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E12} \
+        -g mm 2> {log.E12}" )
+        shell("macs2 callpeak -f BAM -t {input.E13} \
+        -c {input.E13C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E13} \
+        -g mm 2> {log.E13}" )
+        shell("macs2 callpeak -f BAM -t {input.E14} \
+        -c {input.E14C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E14} \
+        -g mm 2> {log.E14}" )
+        shell("macs2 callpeak -f BAM -t {input.E15} \
+        -c {input.E15C} --keep-dup all \
+        --outdir results_10M/macs2/ -p 0.01 \
+        -n {params.E15} \
+        -g mm 2> {log.E15}" )
+
+# ===============================================================================================
+#  8. Peak QC
+#   > narrow peak counts
+#   > fraction of reads in peaks (convert BAM to bed first then do intersect with peaks)
+# ===============================================================================================
+
+## Convert BAM to tagAlign file for calculating FRiP QC metric (Fraction of reads in peaks)
+rule bamToBed:
+    input:
+        "results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bam"
+    output:
+        "results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bed"
+    log:
+        "results_10M/logs/{case}_{stage}_{mark}.bamToBed"
+    shell:
+        "samtools sort -n {input} | bedtools bamtobed -i - > {output}"
+
+# ## Fraction of reads in peaks
+rule frip:
+    input:
+        bed = "results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bed",
+        peak = "results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.narrowPeak"
+    output:
+        "results_10M/qc/{case}_vs_{control}_{stage}_{mark}.frip.txt"
+    shell:
+        "python2.7 scripts/encode_frip.py {input.bed} {input.peak} > {output}"
+
+# ===============================================================================================
+#  9. Create consensus peaksets for replicates
+# ===============================================================================================
+
+# Based on ENCODE `overlap_peaks.py` - recommended for histone marks.
+# Need to pool peaks first for each replicate
+
+rule overlap_peaks:
+    input:
+        E10= ["results_10M/macs2/ENCFF124UYX_vs_ENCFF157KEH_E10.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF045IPK_vs_ENCFF825AVI_E10.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E11= ["results_10M/macs2/ENCFF760QYZ_vs_ENCFF184CUE_E11.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF717QDV_vs_ENCFF376FGM_E11.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E12= ["results_10M/macs2/ENCFF941QJZ_vs_ENCFF058AUT_E12.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF182ZPF_vs_ENCFF203JQV_E12.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E13= ["results_10M/macs2/ENCFF485UDC_vs_ENCFF117QRC_E13.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF124TAB_vs_ENCFF248PGK_E13.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E14= ["results_10M/macs2/ENCFF665QBJ_vs_ENCFF002HZV_E14.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF724DMU_vs_ENCFF784ORI_E14.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E15= ["results_10M/macs2/ENCFF401BKM_vs_ENCFF182XFG_E15.5_H3K4me3_macs2_peaks.narrowPeak", "results_10M/macs2/ENCFF258KCR_vs_ENCFF727QTS_E15.5_H3K4me3_macs2_peaks.narrowPeak"],
+        E10_pooled="results_10M/macs2/E10.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+        E11_pooled="results_10M/macs2/E11.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+        E12_pooled="results_10M/macs2/E12.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+        E13_pooled="results_10M/macs2/E13.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+        E14_pooled="results_10M/macs2/E14.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+        E15_pooled="results_10M/macs2/E15.5_H3K4me3.pooled.macs2_peaks.narrowPeak",
+    output:
+        E10= "results_10M/macs2/H3K4me3_E10.5_overlap.narrowPeak",
+        E11= "results_10M/macs2/H3K4me3_E11.5_overlap.narrowPeak",
+        E12= "results_10M/macs2/H3K4me3_E12.5_overlap.narrowPeak",
+        E13= "results_10M/macs2/H3K4me3_E13.5_overlap.narrowPeak",
+        E14= "results_10M/macs2/H3K4me3_E14.5_overlap.narrowPeak",
+        E15= "results_10M/macs2/H3K4me3_E15.5_overlap.narrowPeak"
+    run:
+        shell("python2.7 scripts/overlap_peaks.py {input.E10} {input.E10_pooled} {output.E10}")
+        shell("python2.7 scripts/overlap_peaks.py {input.E11} {input.E11_pooled} {output.E11}")
+        shell("python2.7 scripts/overlap_peaks.py {input.E12} {input.E12_pooled} {output.E12}")
+        shell("python2.7 scripts/overlap_peaks.py {input.E13} {input.E13_pooled} {output.E13}")
+        shell("python2.7 scripts/overlap_peaks.py {input.E14} {input.E14_pooled} {output.E14}")
+        shell("python2.7 scripts/overlap_peaks.py {input.E15} {input.E15_pooled} {output.E15}")
+
+rule overlap_frip:
+    input:
+      E10bam = "results_10M/bwa/E10.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E11bam = "results_10M/bwa/E11.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E12bam = "results_10M/bwa/E12.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E13bam = "results_10M/bwa/E13.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E14bam = "results_10M/bwa/E14.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E15bam = "results_10M/bwa/E15.5_H3K4me3_q30.sorted.pooled.dedup.bam",
+      E10bed = "results_10M/macs2/H3K4me3_E10.5_overlap.narrowPeak",
+      E11bed = "results_10M/macs2/H3K4me3_E11.5_overlap.narrowPeak",
+      E12bed = "results_10M/macs2/H3K4me3_E12.5_overlap.narrowPeak",
+      E13bed = "results_10M/macs2/H3K4me3_E13.5_overlap.narrowPeak",
+      E14bed = "results_10M/macs2/H3K4me3_E14.5_overlap.narrowPeak",
+      E15bed = "results_10M/macs2/H3K4me3_E15.5_overlap.narrowPeak"
+    output:
+      E10bed = "results_10M/qc/H3K4me3_E10.5_overlap.frip",
+      E11bed = "results_10M/qc/H3K4me3_E11.5_overlap.frip",
+      E12bed = "results_10M/qc/H3K4me3_E12.5_overlap.frip",
+      E13bed = "results_10M/qc/H3K4me3_E13.5_overlap.frip",
+      E14bed = "results_10M/qc/H3K4me3_E14.5_overlap.frip",
+      E15bed = "results_10M/qc/H3K4me3_E15.5_overlap.frip"
+    run:
+      shell("python2.7 scripts/encode_frip.py {input.E10bam} {input.E10bed} > {output.E10bed}")
+      shell("python2.7 scripts/encode_frip.py {input.E11bam} {input.E11bed} > {output.E11bed}")
+      shell("python2.7 scripts/encode_frip.py {input.E12bam} {input.E12bed} > {output.E12bed}")
+      shell("python2.7 scripts/encode_frip.py {input.E13bam} {input.E13bed} > {output.E13bed}")
+      shell("python2.7 scripts/encode_frip.py {input.E14bam} {input.E14bed} > {output.E14bed}")
+      shell("python2.7 scripts/encode_frip.py {input.E15bam} {input.E15bed} > {output.E15bed}")
+
+
+
+# ===============================================================================================
+#  10. Combine all QC into multiqc output
+# ===============================================================================================
+#
+# rule multiqc:
+#     input:
+        # fastqc
+    #     expand("results_10M/fastqc/{sample}_R1_fastqc.html", sample=all_samples),
+    #     expand("results_10M/fastqc/{sample}_R2_fastqc.html", sample=all_samples),
+    #     expand("results_10M/fastqc/{sample}_R1_fastqc.zip", sample=all_samples),
+    #     expand("results_10M/fastqc/{sample}_R2_fastqc.zip", sample=all_samples),
+    #     # bwa
+    #     expand("results_10M/logs/{sample}.align", sample=all_samples),
+    #     expand("results_10M/logs/{sample}.flagstat.qc", sample=all_samples),
+    #     # preseq
+    #     expand("results_10M/preseq/{sample}.ccurve.txt", sample=all_samples),
+    #     # picard
+    #     expand("results_10M/picard/{sample}_est_lib_complex_metrics.txt", sample=all_samples),
+    #     # deepTools
+    #     "results_10M/deeptools/multibamsum.npz",
+    #     "results_10M/deeptools/multibamsum.tab",
+    #     "results_10M/deeptools/pearsoncor_multibamsum.pdf",
+    #     "results_10M/deeptools/pearsoncor_multibamsum_matrix.txt",
+    #     expand("results_10M/deeptools/{sample}.SeqDepthNorm.bw", sample=all_samples),
+    #     "results_10M/deeptools/multiBAM_fingerprint.pdf",
+    #     "results_10M/deeptools/multiBAM_fingerprint_metrics.txt",
+    #     "results_10M/deeptools/multiBAM_fingerprint_rawcounts.txt",
+    #     "results_10M/deeptools/plot_coverage.pdf",
+    #     "results_10M/deeptools/plot_coverage_rawcounts.tab",
+    #     "results_10M/deeptools/bamPEFragmentSize_hist.pdf",
+    #     "results_10M/deeptools/bamPEFragmentSize_rawcounts.tab",
+    #     # phantomPeaks
+    #     expand("results_10M/phantomPeaks/{sample}.spp.pdf", sample = IPS),
+    #     expand("results_10M/phantomPeaks/{sample}.spp.Rdata", sample = IPS),
+    #     expand("results_10M/phantomPeaks/{sample}.spp.out", sample = IPS),
+    #     # macs2
+    #     expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS),
+    #     expand("results_10M/macs2/{case}_vs_{control}_macs2_peaks.xls", zip, case=IPS, control=INPUTS),
+    #     expand("results_10M/macs2/{case}_vs_{control}_macs2_summits.bed", zip, case=IPS, control=INPUTS),
+    #     expand("results_10M/macs2/{case}-vs-{control}-narrowpeak-count_mqc.json", zip, case=IPS, control=INPUTS),
+    #     expand("results_10M/frip/{case}.frip.txt", case=IPS)
+    # params:
+    #     "results_10M/fastqc/",
+    #     "results_10M/bwa/",
+    #     "results_10M/logs/",
+    #     "results_10M/deeptools/",
+    #     "results_10M/macs2/",
+    #     "results_10M/phantomPeaks/",
+    #     "results_10M/frip/",
+    #     "results_10M/picard/"
+    # output:
+    #     directory("results_10M/multiqc/multiqc_report_data/"),
+    #     "results_10M/multiqc/multiqc_report.html",
+    # log:
+    #     "results_10M/logs/multiqc.log"
+    # shell:
+    #     "multiqc -f {params} -m fastqc -m samtools -m picard -m preseq -m deeptools -m phantompeakqualtools -m macs2 -o results_10M/multiqc/ \
+    #     -n multiqc_report.html 2> {log}"
diff --git a/mouse/Snakefile_H3K27ac b/mouse/Snakefile_H3K27ac
old mode 100644
new mode 100755
index 26d04cf754084623fbb241b94c4e28e8c21ab3bd..d788ad12e217a814eb79941f13b0aa24e85660a4
--- a/mouse/Snakefile_H3K27ac
+++ b/mouse/Snakefile_H3K27ac
@@ -48,14 +48,17 @@ all_samples = IPS + INPUTS
 
 rule all:
     input:
-        #expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+    # Alignment and Filtering in results folder
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
         expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        expand("results_10M/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}.q30.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.q30.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        expand("results/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+    # Merging the BAM replicates for the subsampled dedup BAM files
         "results_10M/bwa/E10.5_H3K27ac_q30.sorted.pooled.dedup.bam",
         "results_10M/bwa/E11.5_H3K27ac_q30.sorted.pooled.dedup.bam",
         "results_10M/bwa/E12.5_H3K27ac_q30.sorted.pooled.dedup.bam",
@@ -80,41 +83,44 @@ rule all:
         "results_10M/logs/input_E13.5_H3K27ac.mergeBAM",
         "results_10M/logs/input_E14.5_H3K27ac.mergeBAM",
         "results_10M/logs/input_E15.5_H3K27ac.mergeBAM",
-        "results_10M/qc/H3K27ac_multibamsum.npz",
-        "results_10M/qc/H3K27ac_multibamsum.tab",
-        "results_10M/qc/H3K27ac_pearsoncor_multibamsum.png",
-        "results_10M/qc/H3K27ac_pearsoncor_multibamsum_matrix.txt",
-        expand("results_10M/qc/{sample}_{stage}_{mark}.SeqDepthNorm.bw", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        "results_10M/qc/H3K27ac_multiBAM_fingerprint.png",
-        "results_10M/qc/H3K27ac_multiBAM_fingerprint_metrics.txt",
-        "results_10M/qc/H3K27ac_multiBAM_fingerprint_rawcounts.txt",
-        "results_10M/qc/H3K27ac_plot_coverage.png",
-        "results_10M/qc/H3K27ac_plot_coverage_rawcounts.tab",
-        #expand("results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/logs/{sample}_{stage}_{mark}.pbc.sort", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}.pbc.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",zip, sample=all_samples, stage=STAGE, mark=MARK),
-        #expand("results_10M/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf", zip, sample=all_samples, stage=STAGE, mark=MARK),
-        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
-        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.xls", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
-        expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_summits.bed", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
-        expand("results_10M/logs/{case}_vs_{control}_{stage}_{mark}_call_peaks_macs2.log", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
-        expand("results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bed", zip, case=IPS, stage=STAGE, mark=MARK),
-        expand("results_10M/logs/{case}_{stage}_{mark}.bamToBed", zip, case=IPS, stage=STAGE, mark=MARK),
-        expand("results_10M/qc/{case}_vs_{control}_{stage}_{mark}.frip.txt", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
-        directory("results_10M/macs2/pooled/"),
-        "results_10M/macs2/H3K27ac_E10.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E11.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E12.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E13.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E14.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E15.5_overlap.narrowPeak",
-        "results_10M/macs2/H3K27ac_E10.5_overlap.frip",
-        "results_10M/macs2/H3K27ac_E11.5_overlap.frip",
-        "results_10M/macs2/H3K27ac_E12.5_overlap.frip",
-        "results_10M/macs2/H3K27ac_E13.5_overlap.frip",
-        "results_10M/macs2/H3K27ac_E14.5_overlap.frip",
-        "results_10M/macs2/H3K27ac_E15.5_overlap.frip",
+    # Quality controls for deepTools
+        #"results_10M/qc/H3K27ac_multibamsum.npz",
+        #"results_10M/qc/H3K27ac_multibamsum.tab",
+        #"results_10M/qc/H3K27ac_pearsoncor_multibamsum.pdf",
+        #"results_10M/qc/H3K27ac_pearsoncor_multibamsum_matrix.txt",
+        #expand("results_10M/qc/{sample}_{stage}_{mark}.SeqDepthNorm.bw", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        #"results_10M/qc/H3K27ac_multiBAM_fingerprint.pdf",
+        #"results_10M/qc/H3K27ac_multiBAM_fingerprint_metrics.txt",
+        #"results_10M/qc/H3K27ac_multiBAM_fingerprint_rawcounts.txt",
+        #"results_10M/qc/H3K27ac_plot_coverage.pdf",
+        #"results_10M/qc/H3K27ac_plot_coverage_rawcounts.tab",
+    # Quality control non-redundant fraction etc.
+        #expand("results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        #expand("results/logs/{sample}_{stage}_{mark}.pbc.sort", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        #expand("results/qc/{sample}_{stage}_{mark}.pbc.qc", zip, sample=all_samples, stage=STAGE, mark=MARK),
+        #expand("results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",zip, sample=all_samples, stage=STAGE, mark=MARK),
+        #expand("results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf", zip, sample=all_samples, stage=STAGE, mark=MARK),
+    # MACS2 peak calling    
+        #expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.narrowPeak", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        #expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_peaks.xls", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        #expand("results_10M/macs2/{case}_vs_{control}_{stage}_{mark}_macs2_summits.bed", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        #expand("results_10M/logs/{case}_vs_{control}_{stage}_{mark}_call_peaks_macs2.log", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        #expand("results_10M/bwa/{case}_{stage}_{mark}_q30.sorted.dedup.bed", zip, case=IPS, stage=STAGE, mark=MARK),
+        #expand("results_10M/logs/{case}_{stage}_{mark}.bamToBed", zip, case=IPS, stage=STAGE, mark=MARK),
+        #expand("results_10M/qc/{case}_vs_{control}_{stage}_{mark}.frip.txt", zip, case=IPS, control=INPUTS, stage=STAGE, mark=MARK),
+        #directory("results_10M/macs2/pooled/"),
+        #"results_10M/macs2/H3K27ac_E10.5_overlap.narrowPeak",
+        #"results_10M/macs2/H3K27ac_E11.5_overlap.narrowPeak",
+        #"results_10M/macs2/H3K27ac_E12.5_overlap.narrowPeak",
+        #"results_10M/macs2/H3K27ac_E13.5_overlap.narrowPeak",
+        #"results_10M/macs2/H3K27ac_E14.5_overlap.narrowPeak",
+        #"results_10M/macs2/H3K27ac_E15.5_overlap.narrowPeak",
+        #"results_10M/qc/H3K27ac_E10.5_overlap.frip",
+        #"results_10M/qc/H3K27ac_E11.5_overlap.frip",
+        #"results_10M/qc/H3K27ac_E12.5_overlap.frip",
+        #"results_10M/qc/H3K27ac_E13.5_overlap.frip",
+        #"results_10M/qc/H3K27ac_E14.5_overlap.frip",
+        #"results_10M/qc/H3K27ac_E15.5_overlap.frip",
         # directory("results_10M/multiqc/multiqc_report_data/"),
         # "results_10M/multiqc/multiqc_report.html"
 
@@ -155,36 +161,47 @@ rule all:
 
 rule filter:
     input:
-        "results_10M/bwa/{sample}_{stage}_{mark}.bam"
+        "results/bwa/{sample}_{stage}_{mark}.bam"
     output:
-        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
     log:
-        "results_10M/logs/{sample}_{stage}_{mark}.filter"
+        "results/logs/{sample}_{stage}_{mark}.filter"
     shell:
         "samtools view -b -F 1804 -q 30 {input} | samtools sort -o {output} - 2> {log}"
 
 rule markDups:
     input:
-        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam"
     output:
-        bam="results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
-        dupQC="results_10M/bwa/{sample}_{stage}_{mark}.dupmark.qc"
+        bam="results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
+        dupQC="results/bwa/{sample}_{stage}_{mark}.dupmark.qc"
     log:
-        "results_10M/logs/{sample}_{stage}_{mark}.dedup"
+        "results/logs/{sample}_{stage}_{mark}.dedup"
     shell:
         "picard MarkDuplicates I={input} O={output.bam} \
         METRICS_FILE={output.dupQC} REMOVE_DUPLICATES=false ASSUME_SORTED=true 2> {log}"
 
 rule dedup:
     input:
-        "results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+        "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
     output:
-        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
     log:
-        "results_10M/logs/{sample}_{stage}_{mark}.dedup"
+        "results/logs/{sample}_{stage}_{mark}.dedup"
     shell:
         "samtools view -F 1804 -b {input} | samtools sort -o {output} - 2> {log}"
 
+rule normalise10Mreads:
+    input:
+        "results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    output:
+        "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
+    shell:
+        """
+        frac=$( samtools idxstats {input} | cut -f3 | awk 'BEGIN {total=0} {total += $1} END {frac=10000000/total; if (frac > 1) {print 1} else {print frac}}' )\
+        samtools view -bs $frac {input} > {output}
+        """
+
 rule indexBam:
     input:
         "results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"
@@ -259,50 +276,51 @@ rule mergeBAMreplicates:
 
 rule mappingStats:
     input:
-        a="results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam",
-        #b="results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
-        #c="results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.bam",
-        #d="results_10M/bwa/{sample}_{stage}_{mark}.bam"
+        a="results/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam",
+        b="results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam",
+        c="results/bwa/{sample}_{stage}_{mark}_q30.sorted.bam",
+        d="results/bwa/{sample}_{stage}_{mark}.bam"
     output:
-        a="results_10M/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc",
-      #  b="results_10M/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc",
-      #  c="results_10M/qc/{sample}_{stage}_{mark}.q30.flagstat.qc",
-      #  d="results_10M/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc",
+        a="results/qc/{sample}_{stage}_{mark}.dedup.flagstat.qc",
+        b="results/qc/{sample}_{stage}_{mark}.dupmark.flagstat.qc",
+        c="results/qc/{sample}_{stage}_{mark}.q30.flagstat.qc",
+        d="results/qc/{sample}_{stage}_{mark}.unfiltered.flagstat.qc",
     run:
         shell("samtools flagstat {input.a} > {output.a}")
-      #  shell("samtools flagstat {input.b} > {output.b}")
-      #  shell("samtools flagstat {input.c} > {output.c}")
-      #  shell("samtools flagstat {input.d} > {output.d}")
+        shell("samtools flagstat {input.b} > {output.b}")
+        shell("samtools flagstat {input.c} > {output.c}")
+        shell("samtools flagstat {input.d} > {output.d}")
 
 
 rule sort_name:
      input:
-         "results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+         "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
      output:
-         tmp = "results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
+         tmp = "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
      log:
-         "results_10M/logs/{sample}_{stage}_{mark}.pbc.sort"
+         "results/logs/{sample}_{stage}_{mark}.pbc.sort"
      run:
          shell("samtools sort -n {input} -o {output.tmp} 2> {log}")
 
 rule estimate_lib_complexity:
      input:
-         "results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
+         "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.tmp.bam"
      output:
-         qc = "results_10M/qc/{sample}_{stage}_{mark}.pbc.qc",
+         qc = "results/qc/{sample}_{stage}_{mark}.pbc.qc",
      log:
-         "results_10M/logs/{sample}_{stage}_{mark}.pbc"
+         "results/logs/{sample}_{stage}_{mark}.pbc"
      shell:
-         """
-         bedtools bamtobed -i {input} \
-         | awk 'BEGIN{{OFS="\\t"}}{{print $1,$2,$4,$6}}' \
-         | sort | uniq -c \
-         | awk 'BEGIN{{mt=0;m0=0;m1=0;m2=0}} ($1==1){{m1=m1+1}} ($1==2){{m2=m2+1}} \
-         {{m0=m0+1}} {{mt=mt+$1}} END{{printf "%d\\t%d\\t%d\\t%d\\t%f\\t%f\\t%f\\n" ,mt,m0,m1,m2,m0/mt,m1/m0,m1/m2}}' \
-         > {output.qc}
-         """
-
-## convert to bedPE
+        """
+        bedtools bamtobed -i {input} | grep -v 'chrM' \
+        | awk 'BEGIN{{OFS="\\t"}}{{print $1,$2,$3,$6}}' \
+        | sort | uniq -c \
+        | awk 'BEGIN{{mt=0;m0=0;m1=0;m2=0}} ($1==1){{m1=m1+1}} ($1==2){{m2=m2+1}} \
+        {{m0=m0+1}} {{mt=mt+$1}} END{{printf "%d\\t%d\\t%d\\t%d\\t%f\\t%f\\t%f\\n" ,mt,m0,m1,m2,m0/mt,m1/m0,m1/m2}}' \
+        > {output.qc}
+        """
+
+
+## convert to bed
 ## print read 1 scaffold, read 1 start coordinate, read 2 scaffold, read 2 end coordinate, strand read 1, strand read 2
 ## remove mitochondrial genome
 ## sort by position and obtain uniq reads
@@ -338,7 +356,7 @@ rule deeptools_summary:
 rule deeptools_correlation:
     input: "results_10M/qc/H3K27ac_multibamsum.npz"
     output:
-        fig="results_10M/qc/H3K27ac_pearsoncor_multibamsum.png",
+        fig="results_10M/qc/H3K27ac_pearsoncor_multibamsum.pdf",
         matrix="results_10M/qc/H3K27ac_pearsoncor_multibamsum_matrix.txt"
     log:
         "results_10M/logs/H3K27ac_correlation.deepTools"
@@ -369,7 +387,7 @@ rule deeptools_fingerprint:
         bam = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"], zip, sample=all_samples, stage=STAGE, mark = MARK),
         bai = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"], zip, sample=all_samples, stage=STAGE, mark = MARK)
     output:
-        fig="results_10M/qc/H3K27ac_multiBAM_fingerprint.png",
+        fig="results_10M/qc/H3K27ac_multiBAM_fingerprint.pdf",
         metrics="results_10M/qc/H3K27ac_multiBAM_fingerprint_metrics.txt",
         rawcounts="results_10M/qc/H3K27ac_multiBAM_fingerprint_rawcounts.txt"
     threads: 32
@@ -390,7 +408,7 @@ rule deeptools_plotCoverage:
         bam = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bam"], zip, sample=all_samples, stage=STAGE, mark=MARK),
         bai = expand(["results_10M/bwa/{sample}_{stage}_{mark}_q30.sorted.dedup.bai"], zip, sample=all_samples, stage=STAGE, mark=MARK)
     output:
-        fig="results_10M/qc/H3K27ac_plot_coverage.png",
+        fig="results_10M/qc/H3K27ac_plot_coverage.pdf",
         rawcounts="results_10M/qc/H3K27ac_plot_coverage_rawcounts.tab"
     log:
         "results_10M/logs/H3K27ac_plotCoverage.deepTools"
@@ -407,9 +425,9 @@ rule deeptools_plotCoverage:
 
 rule bamtobed_crossC:
     input:
-        "results_10M/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
+        "results/bwa/{sample}_{stage}_{mark}_q30.dupmark.bam"
     output:
-        tagAlign = "results_10M/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
+        tagAlign = "results/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
     shell:
         """
         bedtools bamtobed -i {input} | \
@@ -421,10 +439,10 @@ rule bamtobed_crossC:
 ## Estimate read length from first 100 reads
 rule subsample_aligned_reads:
     input:
-        "results_10M/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
+        "results/bed/{sample}_{stage}_{mark}_q30_SE.tagAlign.gz"
     output:
-        subsample = "results_10M/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz",
-        tmp = "results_10M/bed/{sample}_{stage}_{mark}_R1_trimmed_q30_SE.tagAlign.tmp"
+        subsample = "results/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz",
+        tmp = "results/bed/{sample}_{stage}_{mark}_R1_trimmed_q30_SE.tagAlign.tmp"
     params:
         nreads= 15000000
     run:
@@ -433,7 +451,7 @@ rule subsample_aligned_reads:
 
 # Determine exclusion range for fragment length estimation.
 ## From bamPEFragmentSize (deepTools) average fragment length is ~220bp
-## See bamPEFragmentSize.histogram.png
+## See bamPEFragmentSize.histogram.pdf
 # Use a fixed lowerbound at -500.
 # Upperbound E
 #EXCLUSION_RANGE_MAX is
@@ -443,12 +461,12 @@ rule subsample_aligned_reads:
 
 rule cross_correlation_SSP:
     input:
-        "results_10M/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz"
+        "results/bed/{sample}_{stage}_{mark}.filt.sample.15Mreads.SE.tagAlign.gz"
     output:
-        CC_SCORES_FILE="results_10M/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",
-        CC_PLOT_FILE="results_10M/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf"
+        CC_SCORES_FILE="results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.qc",
+        CC_PLOT_FILE="results/qc/{sample}_{stage}_{mark}_filt_15Mreads.SE.cc.plot.pdf"
     log:
-        "results_10M/logs/{sample}_{stage}_{mark}_filt_15Mreads.SE.spp.log"
+        "results/logs/{sample}_{stage}_{mark}_filt_15Mreads.SE.spp.log"
     params:
         EXCLUSION_RANGE_MIN=-500,
         EXCLUSION_RANGE_MAX=85
@@ -623,12 +641,12 @@ rule overlap_frip:
       E14bed = "results_10M/macs2/H3K27ac_E14.5_overlap.narrowPeak",
       E15bed = "results_10M/macs2/H3K27ac_E15.5_overlap.narrowPeak"
     output:
-      E10bed = "results_10M/macs2/H3K27ac_E10.5_overlap.frip",
-      E11bed = "results_10M/macs2/H3K27ac_E11.5_overlap.frip",
-      E12bed = "results_10M/macs2/H3K27ac_E12.5_overlap.frip",
-      E13bed = "results_10M/macs2/H3K27ac_E13.5_overlap.frip",
-      E14bed = "results_10M/macs2/H3K27ac_E14.5_overlap.frip",
-      E15bed = "results_10M/macs2/H3K27ac_E15.5_overlap.frip"
+      E10bed = "results_10M/qc/H3K27ac_E10.5_overlap.frip",
+      E11bed = "results_10M/qc/H3K27ac_E11.5_overlap.frip",
+      E12bed = "results_10M/qc/H3K27ac_E12.5_overlap.frip",
+      E13bed = "results_10M/qc/H3K27ac_E13.5_overlap.frip",
+      E14bed = "results_10M/qc/H3K27ac_E14.5_overlap.frip",
+      E15bed = "results_10M/qc/H3K27ac_E15.5_overlap.frip"
     run:
       shell("python2.7 scripts/encode_frip.py {input.E10bam} {input.E10bed} > {output.E10bed}")
       shell("python2.7 scripts/encode_frip.py {input.E11bam} {input.E11bed} > {output.E11bed}")
@@ -660,15 +678,15 @@ rule overlap_frip:
     #     # deepTools
     #     "results_10M/deeptools/multibamsum.npz",
     #     "results_10M/deeptools/multibamsum.tab",
-    #     "results_10M/deeptools/pearsoncor_multibamsum.png",
+    #     "results_10M/deeptools/pearsoncor_multibamsum.pdf",
     #     "results_10M/deeptools/pearsoncor_multibamsum_matrix.txt",
     #     expand("results_10M/deeptools/{sample}.SeqDepthNorm.bw", sample=all_samples),
-    #     "results_10M/deeptools/multiBAM_fingerprint.png",
+    #     "results_10M/deeptools/multiBAM_fingerprint.pdf",
     #     "results_10M/deeptools/multiBAM_fingerprint_metrics.txt",
     #     "results_10M/deeptools/multiBAM_fingerprint_rawcounts.txt",
-    #     "results_10M/deeptools/plot_coverage.png",
+    #     "results_10M/deeptools/plot_coverage.pdf",
     #     "results_10M/deeptools/plot_coverage_rawcounts.tab",
-    #     "results_10M/deeptools/bamPEFragmentSize_hist.png",
+    #     "results_10M/deeptools/bamPEFragmentSize_hist.pdf",
     #     "results_10M/deeptools/bamPEFragmentSize_rawcounts.tab",
     #     # phantomPeaks
     #     expand("results_10M/phantomPeaks/{sample}.spp.pdf", sample = IPS),
diff --git a/mouse/Snakefile_H3K4me3 b/mouse/Snakefile_H3K4me3
old mode 100644
new mode 100755
diff --git a/mouse/configs/.multiqc_config.yaml b/mouse/configs/.multiqc_config.yaml
old mode 100644
new mode 100755
diff --git a/mouse/configs/SRR_H3K27ac.txt b/mouse/configs/SRR_H3K27ac.txt
old mode 100644
new mode 100755
diff --git a/mouse/configs/SRR_H3K4me3.txt b/mouse/configs/SRR_H3K4me3.txt
old mode 100644
new mode 100755
diff --git a/mouse/configs/cluster.json b/mouse/configs/cluster.json
old mode 100644
new mode 100755
diff --git a/mouse/configs/config_H3K27ac.yaml b/mouse/configs/config_H3K27ac.yaml
old mode 100644
new mode 100755
diff --git a/mouse/configs/config_H3K4me3.yaml b/mouse/configs/config_H3K4me3.yaml
old mode 100644
new mode 100755
diff --git a/mouse/envs/chip_environment.yml b/mouse/envs/chip_environment.yml
old mode 100644
new mode 100755
diff --git a/mouse/scripts/count_peaks.py b/mouse/scripts/count_peaks.py
old mode 100644
new mode 100755
diff --git a/mouse/scripts/encode_frip.py b/mouse/scripts/encode_frip.py
old mode 100644
new mode 100755
diff --git a/mouse/scripts/overlap_peaks.py b/mouse/scripts/overlap_peaks.py
old mode 100644
new mode 100755
diff --git a/mouse/scripts/subsample.sh b/mouse/scripts/subsample.sh
old mode 100644
new mode 100755
diff --git a/peakFeature2.R b/peakFeature2.R
new file mode 100644
index 0000000000000000000000000000000000000000..812a4e5b1668e7bdde1c6c654c4dbdbaf8ba5f6a
--- /dev/null
+++ b/peakFeature2.R
@@ -0,0 +1,393 @@
+## This script looks through various features of the dunnart peaks including
+## Peak intensity, lengths, distance to TSS, filtering peaks.
+
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(multcompView)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation")
+
+font = theme(axis.text.x = element_text(size = 25),
+        axis.text.y = element_text(size = 25),  
+        axis.title.x = element_text(size = 25),
+        axis.title.y = element_text(size = 25), 
+        legend.title=element_text(size=25), legend.text=element_text(size=25))
+
+plot_dir='/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus'
+
+###########################################################################################################
+############################################## Peak Features ##############################################
+###########################################################################################################
+
+unfilteredPeakFeatures <- function(fileList, combined.tbl.stack, peak.length.plot, peak.intensity.plot){
+    files =list.files(fileList, pattern= ".narrowPeak", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")))
+    names(data) = c("enhancers","promoters","H3K27ac_only","H3K27ac","H3K4me3_H3K27ac", "H3K4me3_only","H3K4me3")
+    df1 = Map(mutate, data, group = names(data))
+    df1$H3K27ac_only$cre = "enhancers"
+    df1$H3K4me3_only$cre = "promoters"
+    df1$H3K4me3_H3K27ac$cre = "promoters"
+
+    fullPeaks = rbind(df1$enhancers, df1$promoters, df1$H3K27ac, df1$H3K4me3)
+    combinedPeaks = rbind(df1$H3K27ac_only, df1$H3K4me3_H3K27ac, df1$H3K4me3_only)
+        
+    ## Plot stacked bar graph with number of peaks for each histone mark
+    combined.tbl <- with(combinedPeaks, table(group, cre))
+    combined.tbl <- as.data.frame(combined.tbl)
+    pdf(file=paste0(plot_dir, combined.tbl.stack), width = 10, height = 7)
+      ggplot(combined.tbl, aes(factor(cre), Freq, fill=group)) + 
+      geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
+      scale_fill_manual(values = c("#FF9D40",  "#37B6CE", "#04859D"))  + 
+      theme_minimal() + ylab("Number of peaks") +
+      xlab("")
+    dev.off()
+
+    # Peak lengths for H3K4me3 and H3K27ac
+    fullPeaks$length <- (fullPeaks$V3 - fullPeaks$V2)
+    level_order <- c('promoter', 'enhancer', 'H3K4me3', 'H3K27ac')
+
+    ## Plot peak lengths
+    p <- ggplot(fullPeaks, aes(factor(group, level = level_order), y = log10(length))) + 
+    geom_half_violin(aes(fill=factor(group), color=factor(group)),
+    side = "r", 
+    position = "dodge"
+    )+
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#FCF8EC","#F5F6F7","#E4F3F1"),
+    position = position_dodge(width=.1)
+    ) +
+    coord_cartesian(xlim = c(1.2, NA), clip = "off") + theme_bw() + xlab("") + ylab("Log10 Peak Length") 
+    pdf(file=paste0(plot_dir, peak.length.plot), width = 10, height = 7)
+    print(p +  scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
+    scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
+    dev.off()
+
+    ## Plot peak intensity
+    p <- ggplot(fullPeaks, aes(factor(group, level = level_order), y = log10(V7))) + 
+    geom_half_violin(aes(fill=factor(group), color=factor(group)),
+    side = "r", 
+    position = "dodge"
+    )+
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#FCF8EC","#F5F6F7","#E4F3F1"),
+    position = position_dodge(width=.1)
+    ) +
+    coord_cartesian(xlim = c(1.2, NA), clip = "off") + theme_bw() + xlab("") + ylab("Log10 Peak Intensity") 
+    pdf(file=paste0(plot_dir, peak.intensity.plot), width = 10, height = 7)
+    print(p +  scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
+    scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
+    dev.off()  
+}
+
+unfilteredPeakFeatures(fileList = fullPeak_dir, combined.tbl.stack= "dunnart_combined_stacked.pdf",
+peak.length.plot = "dunnart_fullPeaks_length.pdf", peak.intensity.plot = "dunnart_fullPeaks_intensity.pdf")
+
+## Now see where the peaks are located in relation to the TSS
+## Promoters should be reasonably close to the TSS and enhancers more distal to the TSS
+## Plot distance to TSS for all unfiltered peaks 
+
+distanceToTSS <- function(fileList, enhancerTSSplot, promoterTSSplot, TSSplot){
+    files =list.files(fileList, pattern= "annotation.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(data) = c("enhancers","promoters","H3K27ac","H3K4me3")
+    df1 = Map(mutate, data, mark = names(data))
+    enhProm = list(df1$promoters, df1$enhancers)
+    enhProm = suppressWarnings(lapply(enhProm, function(x) x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
+    names(enhProm) = c("promoters","enhancers")
+
+    pdf(file = paste0(plot_dir, enhancerTSSplot), width=10, height = 7)
+    print(ggplot(enhProm$enhancers, aes(x=log10_abs_dist)) + 
+    geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + 
+    scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw())
+    dev.off()
+
+    pdf(file = paste0(plot_dir, promoterTSSplot), width=10, height = 7)
+    print(ggplot(enhProm$promoters, aes(x=log10_abs_dist)) + 
+    geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + 
+    scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw())
+    dev.off()
+
+    enhProm = rbindlist(enhProm,)
+    pdf(file = paste0(plot_dir, TSSplot), width=10, height = 7)
+    print(ggplot(enhProm, aes(x=log10_abs_dist, fill=factor(mark), color=factor(mark))) + 
+    geom_density(alpha=0.5) +scale_color_manual(values=c("#E9C46A", "#2A9D8F")) + scale_fill_manual(values=c("#E9C46A", "#2A9D8F")) + 
+    scale_x_continuous(limits=c(-7, 7)) + theme_bw() + font)
+    dev.off()
+
+}
+distanceToTSS(fileList = annot_dir, enhancerTSSplot="dunnart_enhancerTSS.pdf", 
+promoterTSSplot="dunnart_promoterTSS.pdf", TSSplot = "dunnart_TSS.pdf")
+
+#########################################################################################################################
+################################################### K-MEAN CLUSTERING ###################################################
+#########################################################################################################################
+### From this we can see that the promoter peaks have a large amount of peaks a long way from the TSS
+### Suggests that these are either actually enhancers or they represent unannotated transcripts
+### Probably a mixture of both based on comparison with mouse peaks (where the annotation is better) there are not as many
+### peaks in this distal regions. 
+### Use k-means clustering to group the peaks to decide on a cut off for "promoter" peaks. This will be more conservative for
+### what we identify as promoters.
+
+font = theme(axis.text.x = element_text(size = 25),
+        axis.text.y = element_text(size = 25),  
+        axis.title.x = element_text(size = 25),
+        axis.title.y = element_text(size = 25), 
+        legend.title=element_text(size=25), legend.text=element_text(size=25))
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus")
+
+## k-means clustering 
+
+kmeansCluster <- function(file, plot1, plot2, output){
+  data = fread(file, header=TRUE, sep="\t", quote = "") # read in all files
+  data = data[,log10_dist:=log10(abs(distanceToTSS)+1.1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
+  data = data[,abs_dist:=log10(abs(distanceToTSS)+1.1)]
+
+  data = data %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation")
+  
+  ## plot the number of peaks in each cluster
+  ## Using the MacQueen algorithm instead of the default Lloyd 
+  ## The algorithm is more efficient as it updates centroids more often and usually needs to
+  ## perform one complete pass through the cases to converge on a solution.
+  df = data[,5]
+  cre.kmeans = kmeans(df, 3, iter.max=100, nstart=25, algorithm="MacQueen")  
+  cre.kmeans_table = data.frame(cre.kmeans$size, cre.kmeans$centers)
+  cre.kmeans_df = data.frame(Cluster = cre.kmeans$cluster, data)
+
+  pdf(plot1, width=10, height = 7)
+  p <- ggplot(data = cre.kmeans_df, aes(y = Cluster, fill=as.factor(Cluster), color=as.factor(Cluster))) +
+  geom_bar()  +
+  theme(plot.title = element_text(hjust = 0.5)) + theme_bw() + font
+  pdf(plot1)
+  print(p + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) + scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")))
+  dev.off()
+
+
+  p <- ggplot(cre.kmeans_df, aes(x=log10_dist, fill=as.factor(Cluster), color=as.factor(Cluster))) + 
+            geom_histogram(binwidth=0.1, position = 'identity')  +
+            theme_bw()+ font
+  pdf(plot2, width = 10, height = 7)
+  print(p + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) + scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")))
+  dev.off()  
+
+  write.table(cre.kmeans_df, output, sep="\t", quote=F, row.names=F)
+  return(cre.kmeans_df)
+}
+promoter_kmeans = kmeansCluster(file = "consensus/promoters/annotation/dunnart_promoter_annotation.txt",  plot1 = "promoter_kmeans_bar.pdf", plot2 = "promoter_kmeans_histogram.pdf", output = "promoter_kmeans_peaks.txt")
+
+## Extract cluster groups from narrowPeak files and save separately
+cluster1 <- promoter_kmeans$V4[promoter_kmeans$Cluster==1]
+cluster2 <- promoter_kmeans$V4[promoter_kmeans$Cluster==2]
+cluster3 <- promoter_kmeans$V4[promoter_kmeans$Cluster==3]
+
+extractClusters = function(promoter, cluster1, cluster2, cluster3, out1, out2, out3){
+  file= fread(promoter, header=FALSE, sep="\t", quote = "") 
+  write.table(subset(file, V4 %in% cluster1), out1, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+  write.table(subset(file, V4 %in% cluster2), out2, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+  write.table(subset(file, V4 %in% cluster3), out3, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
+}
+
+extractClusters(promoter = "consensus/promoters/dunnart_promoter_peaks.narrowPeak", out1 = "cluster1_promoters.narrowPeak", 
+out2 ="cluster2_promoters.narrowPeak" , out3 = "cluster3_promoters.narrowPeak", cluster1 = cluster1, 
+cluster2 = cluster2, cluster3 = cluster3)
+
+## Filter mouse peaks two the same range as the dunnart clusters
+filterMousePeaks = function(fileList, plot_dir){
+  files =list.files(fileList, pattern= "5_promoter_peaks.narrowPeak", full.names=T) # create list of files in directory
+  files = as.list(files)
+  data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  names(data) = c("E10","E11","E12","E13","E14", "E15")
+  df1 = Map(mutate, data, group = names(data))
+  df1 = lapply(df1, function(x) x %>% filter(x$distanceToTSS<200 & x$distanceToTSS>-200))    
+  lapply(names(df1), function(x) write.table(df1[[x]], file=paste(plot_dir,x,"filtered_annotation","txt", sep="."), sep="\t", quote=FALSE, col.names=TRUE, row.names=FALSE))
+}
+filterMousePeaks(fileList = "consensus/promoters/annotation", plot_dir = "consensus/promoters/clustered/")
+
+#########################################################################################################################
+################################################ FILTERED PEAK FEATURES #################################################
+#########################################################################################################################
+
+## Assess peak features after k-means clustering
+
+peakFeatures <- function(fileList, peak.length.plot, peak.intensity.plot){
+    files =list.files(fileList, pattern= "annotation.txt", full.names=T) # create list of files in directory
+    files = as.list(files)
+    data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote="", na.strings=c("", "NA")))
+    names(data) = c("dunnart", "E10","E11","E12","E13","E14", "E15")
+    df = Map(mutate, data, group = names(data))
+    df = lapply(df, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
+    df = lapply(df, function(x) x %>% dplyr::select("width", "V7", "annotation", "mouseensembl", "distanceToTSS", "group") %>% as.data.table())
+
+    peaks = rbindlist(df,)
+    peaks$cre = "promoters"
+
+    enhancers = list.files(enhancerList, pattern= "annotation.txt", full.names=T)
+    enhancers = as.list(enhancers)
+    enhancers = lapply(enhancers, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+    names(enhancers) = c("dunnart", "E10","E11","E12","E13","E14", "E15")
+    enhancers = Map(mutate, enhancers, group = names(enhancers))
+    enhancers = lapply(enhancers, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
+    enhancers = lapply(enhancers, function(x) x %>% dplyr::select("width", "V7", "annotation", "mouseensembl", "distanceToTSS", "group") %>% as.data.table())
+
+    enhancers = rbindlist(enhancers,)
+    enhancers$cre = "enhancers"
+
+    all = rbind(peaks, enhancers)
+
+    ## Plot peak intensity
+    p <- ggplot(all, aes(factor(group), y = log10(V7))) + 
+    geom_violin(aes(fill=factor(group), color=factor(group)), position = "dodge") + 
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, outlier.shape = NA,
+    #fill=c("#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC"),
+    fill=c("#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2"),
+    #fill=c("#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1"),
+    position = position_dodge(width=.1), 
+    notch=TRUE) + facet_wrap(. ~ cre, strip.position = "bottom") +
+    theme(panel.spacing = unit(0, "lines"),
+        strip.background = element_blank(),
+        strip.placement = "outside", axis.text.x = element_text(angle = 45, , hjust = 1, size = 25) 
+        ) + theme_bw() + xlab("") + ylab("Log 10 Peak Intensity") 
+    
+    pdf("test.pdf", width=10, height = 6)
+    print(p + scale_fill_manual(values = c("#FFDD8C","#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078"))
+     + scale_color_manual(values = c("#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166"))) 
+    #scale_color_manual(values = c("#E9C46A","#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A")) +
+    #scale_color_manual(values = c("#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B")) +
+    #scale_color_manual(values = c("#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F")) +
+    #scale_fill_manual(values = c("#F1DAA2","#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2")))
+    #scale_fill_manual(values = c("#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078")))
+    #scale_fill_manual(values = c("#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9")))
+    dev.off()
+
+    ## Plot peak lengths
+    p <- ggplot(all, aes(factor(group), y = log10(width))) + 
+    geom_violin(aes(fill=factor(group), color=factor(group)), position = "dodge") + 
+    geom_boxplot(aes(color=factor(group)),
+    width = .15, outlier.shape = NA,
+    #fill=c("#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC"),
+    fill=c("#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2"),
+    #fill=c("#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1"),
+    position = position_dodge(width=.1), 
+    notch=TRUE) + facet_wrap(. ~ cre, strip.position = "bottom") +
+  theme(panel.spacing = unit(0, "lines"),
+        strip.background = element_blank(),
+        strip.placement = "outside", axis.text.x = element_text(angle = 45, , hjust = 1, size = 25) 
+        ) + theme_bw() + xlab("") + ylab("Log 10 Peak Length") 
+    
+    pdf("test.pdf", width=10, height = 6)
+    print(p +  scale_fill_manual(values = c("#FFDD8C","#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078"))
+     + scale_color_manual(values = c("#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166"))) 
+    #scale_color_manual(values = c("#E9C46A","#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A")) +
+    #scale_color_manual(values = c("#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B", "#4D004B")) +
+    #scale_color_manual(values = c("#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F")) +
+    #scale_fill_manual(values = c("#F1DAA2","#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2")))
+    #scale_fill_manual(values = c("#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078", "#7A4078")))
+    #scale_fill_manual(values = c("#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9"))
+    dev.off()
+
+    ## distance to TSS
+    peaks = peaks[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+    pdf(file = "clusteredPromoter_TSS.pdf", width=10, height = 7)
+    print(ggplot(peaks, aes(x=distanceToTSS, color = factor(group), fill = factor(group))) + 
+    geom_density(alpha=0.5) + theme_bw()+ scale_y_continuous(limits=c(0,0.15), labels = scales::percent))
+    dev.off()
+}
+
+peakFeatures(fileList = "consensus/promoters/clustered", enhancerList = "consensus/enhancers/annotation", peak.length.plot = "dunnart_cluster_length.pdf", 
+peak.intensity.plot = "dunnart_cluster_intensity.pdf")
+
+peakFeatures(fileList = "consensus/enhancers/annotation", peak.length.plot = "dunnart_enhancers_length.pdf", 
+peak.intensity.plot = "dunnart_enhancer_intensity.pdf")
+
+## Now look at CpG% and GC% for these groups
+
+GCcontent = function(fileList, clusterList, enhancerList, gcPlot, cpgPlot){
+  files =list.files(fileList, pattern= "_homerAnnot.txt", full.names=T) # create list of files in directory
+  files = as.list(files)
+  data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  names(data) = c("dunnart", "E10","E11","E12","E13","E14", "E15")
+  df1 = Map(mutate, data, group = names(data))
+  clusterFiles = list.files(clusterList, pattern= "_annotation.txt", full.names=T)
+  clusterData = lapply(clusterFiles, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  clusterIDs = lapply(clusterData, function(x) x$V4 %>% as.data.frame())
+  clusterIDs = rbindlist(clusterIDs,)
+  df1 = rbindlist(df1,)
+  test = as.vector(unlist(clusterIDs))
+  promoters = df1[df1$PeakID %in% test,]
+  colnames(promoters)[20] <- "CpG"
+  colnames(promoters)[21] <- "GC"
+  promoters$cre = "promoters"
+
+  enhancers = list.files(enhancerList, pattern= "_homerAnnot.txt", full.names=T)
+  enhancers = as.list(enhancers)
+  enhancers = lapply(enhancers, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  names(enhancers) = c("dunnart", "E10","E11","E12","E13","E14", "E15")
+  enhancers = Map(mutate, enhancers, group = names(enhancers))
+
+  enhancers = rbindlist(enhancers,)
+  colnames(enhancers)[20] <- "CpG"
+  colnames(enhancers)[21] <- "GC"
+  enhancers$cre = "enhancers"
+ 
+  all = rbind(promoters, enhancers)
+
+  p <- ggplot(all, aes(factor(group), y = GC)) + 
+    geom_violin(aes(fill=factor(group), color=factor(group)),
+    position = "dodge"
+    )+
+  geom_boxplot(aes(color=factor(group)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#FFEEC6","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2","#D3BFD2"),
+    position = position_dodge(width=.1)
+  ) + facet_wrap(. ~ cre, strip.position = "bottom") +
+  theme(panel.spacing = unit(0, "lines"),
+        strip.background = element_blank(),
+        strip.placement = "outside", axis.text.x = element_text(angle = 45, , hjust = 1, size = 25) 
+        ) +
+  theme_bw() + xlab("") + ylab("GC content") 
+  pdf(gcPlot, width=10, height = 6)
+  print(p + scale_fill_manual(values = c("#FFDD8C","#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078")) + scale_color_manual(values = c("#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166"))) 
+  dev.off()
+
+  p <- ggplot(all, aes(factor(group), y = CpG)) + 
+  geom_violin(aes(fill=factor(group), color=factor(group)),
+   position = "dodge"
+   )+
+  geom_boxplot(aes(color=factor(group)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    #fill=c("#D3BFD2","#D4C7E2","#E7EEF6", "#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + facet_wrap(. ~ cre, strip.position = "bottom") +
+  theme(panel.spacing = unit(0, "lines"),
+        strip.background = element_blank(),
+        strip.placement = "outside", axis.text.x = element_text(angle = 45, , hjust = 1, size = 25) 
+        ) + theme_bw() + xlab("") + ylab("CpG") 
+  pdf(cpgPlot, width=10, height = 6)
+  print(p + scale_fill_manual(values = c("#FFDD8C","#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C", "#FFDD8C","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078","#7A4078")) + scale_color_manual(values = c("#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#4D004B","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166","#FFD166"))) 
+  dev.off()
+}
+
+GCcontent(fileList = "consensus/promoters/homerAnnot", clusterList = "consensus/promoters/clustered", enhancerList = "consensus/enhancers/homerAnnot", gcPlot ="dunnart_GC.pdf", cpgPlot = "dunnart_cpg.pdf")
\ No newline at end of file
diff --git a/peakFeatures.R b/peakFeatures.R
new file mode 100644
index 0000000000000000000000000000000000000000..ed936f465395a97673774ba06e730a762d1e651e
--- /dev/null
+++ b/peakFeatures.R
@@ -0,0 +1,418 @@
+# Load in libraries
+library(data.table) 
+library(tidyverse)
+library(ggridges)
+library(ggpubr)
+library(reshape2)
+library(RColorBrewer)
+library(ggplot2)
+library(VennDiagram)
+library(viridis)
+library(hrbrthemes)
+library(gghalves)
+library(ComplexHeatmap)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/")
+K4_K27 <- fread("H3K4me3_and_H3K27ac.narrowPeak", header=FALSE)
+K4 <- fread("H3K4me3_only.narrowPeak", header=FALSE)
+K27 <- fread("H3K27ac_only.narrowPeak", header=FALSE)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/mouse/results_10M/macs2/")
+
+K4_K27.E10 <- fread("H3K4me3_and_H3K27ac_E10.5.narrowPeak")
+K4_K27.E11<- fread("H3K4me3_and_H3K27ac_E11.5.narrowPeak")
+K4_K27.E12<- fread("H3K4me3_and_H3K27ac_E12.5.narrowPeak")
+K4_K27.E13<- fread("H3K4me3_and_H3K27ac_E13.5.narrowPeak")
+K4_K27.E14<- fread("H3K4me3_and_H3K27ac_E14.5.narrowPeak")
+K4_K27.E15<- fread("H3K4me3_and_H3K27ac_E15.5.narrowPeak")
+
+K4.E10<- fread("H3K4me3_E10.5_only.narrowPeak")
+K4.E11<- fread("H3K4me3_E11.5_only.narrowPeak")
+K4.E12<- fread("H3K4me3_E12.5_only.narrowPeak")
+K4.E13<- fread("H3K4me3_E13.5_only.narrowPeak")
+K4.E14<- fread("H3K4me3_E14.5_only.narrowPeak")
+K4.E15<- fread("H3K4me3_E15.5_only.narrowPeak")
+
+K27.E10<- fread("H3K27ac_E10.5_only.narrowPeak")
+K27.E11<- fread("H3K27ac_E11.5_only.narrowPeak")
+K27.E12<- fread("H3K27ac_E12.5_only.narrowPeak")
+K27.E13<- fread("H3K27ac_E13.5_only.narrowPeak")
+K27.E14<- fread("H3K27ac_E14.5_only.narrowPeak")
+K27.E15<- fread("H3K27ac_E15.5_only.narrowPeak")
+
+K4_K27$mark <- "H3K4me3 and H3K27ac"
+K4_K27.E10$mark<- "H3K4me3 and H3K27ac"
+K4_K27.E11$mark<- "H3K4me3 and H3K27ac"
+K4_K27.E12$mark<- "H3K4me3 and H3K27ac"
+K4_K27.E13$mark<- "H3K4me3 and H3K27ac"
+K4_K27.E14$mark<- "H3K4me3 and H3K27ac"
+K4_K27.E15$mark<- "H3K4me3 and H3K27ac"
+
+K4$mark <- "H3K4me3"
+K4.E10$mark<- "H3K4me3"
+K4.E11$mark<- "H3K4me3"
+K4.E12$mark<- "H3K4me3"
+K4.E13$mark<- "H3K4me3"
+K4.E14$mark<- "H3K4me3"
+K4.E15$mark<- "H3K4me3"
+
+K27$mark <- "H3K27ac"
+K27.E10$mark <- "H3K27ac"
+K27.E11$mark <- "H3K27ac"
+K27.E12$mark <- "H3K27ac"
+K27.E13$mark <- "H3K27ac"
+K27.E14$mark <- "H3K27ac"
+K27.E15$mark <- "H3K27ac"
+
+K4_K27$stage <- "P0"
+K4_K27.E10$stage <- "E10.5"
+K4_K27.E11$stage <- "E11.5"
+K4_K27.E12$stage <- "E12.5"
+K4_K27.E13$stage <- "E13.5"
+K4_K27.E14$stage <- "E14.5"
+K4_K27.E15$stage <- "E15.5"
+
+K4$stage <- "P0"
+K4.E10$stage <- "E10.5"
+K4.E11$stage <- "E11.5"
+K4.E12$stage <- "E12.5"
+K4.E13$stage <- "E13.5"
+K4.E14$stage <- "E14.5"
+K4.E15$stage <- "E15.5"
+
+K27$stage <- "P0"
+K27.E10$stage <- "E10.5"
+K27.E11$stage <- "E11.5"
+K27.E12$stage <- "E12.5"
+K27.E13$stage <- "E13.5"
+K27.E14$stage <- "E14.5"
+K27.E15$stage <- "E15.5"
+combinedPeaks <- rbind(K4, K4.E10, K4.E11, K4.E12, K4.E13, K4.E14, K4.E15, K4_K27, K4_K27.E10, K4_K27.E11, K4_K27.E12, K4_K27.E13, K4_K27.E14, K4_K27.E15, K27, K27.E10, K27.E11, K27.E12, K27.E13, K27.E14, K27.E15)
+
+## Plot stacked bar graph with number of peaks for each histone mark
+tbl <- with(combinedPeaks, table(stage, mark))
+data <- as.data.frame(tbl)
+data <- ddply(data, .(mark), transform, pos = cumsum(Freq)-0.2*Freq)
+
+pdf("consensus_peaks.pdf")
+ggplot(data, aes(factor(stage), Freq, fill=mark, label=)) + 
+    geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
+    scale_fill_manual(values = c("#FFD166",  "#264653", "#2A9D8F"))+
+geom_text(data=subset(data,Freq != 0),aes(label=Freq, y=pos), size=3) + 
+    theme_minimal() + ylab("Number of peaks") +
+    xlab("")
+dev.off()
+
+## Peak lengths
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results_10M/macs2/")
+enhancers <- fread("dunnart_enhancer_peaks.narrowPeak", header=FALSE)
+promoters <- fread("dunnart_promoter_peaks.narrowPeak", header=FALSE)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/mouse/results_10M/macs2/")
+
+promoter.E10 <- fread("E10.5_promoter_peaks.narrowPeak")
+promoter.E11<- fread("E11.5_promoter_peaks.narrowPeak")
+promoter.E12<- fread("E12.5_promoter_peaks.narrowPeak")
+promoter.E13<- fread("E13.5_promoter_peaks.narrowPeak")
+promoter.E14<- fread("E14.5_promoter_peaks.narrowPeak")
+promoter.E15<- fread("E15.5_promoter_peaks.narrowPeak")
+
+enhancer.E10 <- fread("E10.5_enhancer_peaks.narrowPeak")
+enhancer.E11<- fread("E11.5_enhancer_peaks.narrowPeak")
+enhancer.E12<- fread("E12.5_enhancer_peaks.narrowPeak")
+enhancer.E13<- fread("E13.5_enhancer_peaks.narrowPeak")
+enhancer.E14<- fread("E14.5_enhancer_peaks.narrowPeak")
+enhancer.E15<- fread("E15.5_enhancer_peaks.narrowPeak")
+
+enhancers$cre <- "enhancer"
+promoters$cre <- "promoter"
+
+promoter.E10$cre <- "promoter"
+promoter.E11$cre <- "promoter"
+promoter.E12$cre <- "promoter"
+promoter.E13$cre <- "promoter"
+promoter.E14$cre <- "promoter"
+promoter.E15$cre <- "promoter"
+
+enhancer.E10$cre <- "enhancer"
+enhancer.E11$cre <- "enhancer"
+enhancer.E12$cre <- "enhancer"
+enhancer.E13$cre <- "enhancer"
+enhancer.E14$cre <- "enhancer"
+enhancer.E15$cre <- "enhancer"
+
+enhancers$stage <- "P0"
+promoters$stage <- "P0"
+
+promoter.E10$stage <- "E10.5"
+promoter.E11$stage <- "E11.5"
+promoter.E12$stage <- "E12.5"
+promoter.E13$stage <- "E13.5"
+promoter.E14$stage <- "E14.5"
+promoter.E15$stage <- "E15.5"
+
+enhancer.E10$stage <- "E10.5"
+enhancer.E11$stage <- "E11.5"
+enhancer.E12$stage <- "E12.5"
+enhancer.E13$stage <- "E13.5"
+enhancer.E14$stage <- "E14.5"
+enhancer.E15$stage <- "E15.5"
+fullPeaks <- rbind(enhancers, enhancer.E10, enhancer.E11, enhancer.E12, enhancer.E13, enhancer.E14, enhancer.E15, promoters, promoter.E10, promoter.E11, promoter.E12, promoter.E13, promoter.E14, promoter.E15)
+fullPeaks$length <- (fullPeaks$V3 - fullPeaks$V2)
+fullPeaks$lengthkb <- fullPeaks$length / 1000
+promoter = dplyr::filter(fullPeaks, cre == "promoter")
+enhancer = dplyr::filter(fullPeaks, cre == "enhancer")
+
+
+level_order <- c('P0', 'E10.5', 'E11.5', 'E12.5', 'E13.5', 'E14.5', 'E15.5')
+## Plot peak lengths
+p <- ggplot(promoter, aes(factor(stage, level = level_order), y = log10(length))) + 
+  geom_half_violin(aes(fill=factor(stage), color=factor(stage)),
+    side = "r", 
+    position = "dodge"
+    )+
+  geom_boxplot(aes(color=factor(stage)),
+    width = .15, 
+    outlier.shape = NA,
+    #fill=c("#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC"),
+    fill=c("#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + gghalves::geom_half_point(
+    side = "l", 
+    #color=c("#E9C46A","#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A"),
+    color=c("#2A9D8F","#2A9D8F","#2A9D8F","#2A9D8F","#2A9D8F","#2A9D8F","#2A9D8F"),
+    range_scale = .2, 
+    alpha = .2) + theme_bw() + xlab("") + ylab("Log 10 length") 
+pdf("promoter_length.pdf", width=9, height = 8)
+p +  
+#scale_color_manual(values = c("#E9C46A","#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A")) +
+scale_color_manual(values = c("#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F")) +
+#scale_fill_manual(values = c("#F1DAA2","#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2"))
+scale_fill_manual(values = c("#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9"))
+dev.off()
+
+"#E9C46A","#E9C46A","#264653","#2A9D8F", "#E9C46A","#264653","#2A9D8F"
+"#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9", "#F1DAA2","#AEBABF","#7AC2B9"
+"#FCF8EC","#F5F6F7","#E4F3F1", "#FCF8EC","#F5F6F7","#E4F3F1"),
+#F1DAA2
+
+# Peak intensity (fold-change) for enhancer/promoters
+p <- ggplot(enhancer, aes(factor(stage, level = level_order), y = log10(length))) + 
+  geom_violin(aes(fill=factor(stage), color=factor(stage)), 
+    position = "dodge"
+    )+
+  geom_boxplot(aes(color=factor(stage)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC", "#FCF8EC"),
+    #fill=c("#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1", "#E4F3F1"),
+    position = position_dodge(width=.1), notch=TRUE
+  )  + theme_bw() + xlab("") + ylab("Log 10 Peak Intensity") 
+pdf("enhancer_length.pdf", width=9, height = 8)
+p +  
+scale_color_manual(values = c("#E9C46A","#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A", "#E9C46A")) +
+#scale_color_manual(values = c("#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F", "#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2", "#F1DAA2"))
+#scale_fill_manual(values = c("#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9", "#7AC2B9"))
+dev.off()
+
+
+## density of peaks distance from TSS
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/enhancers/")
+enhancers <- fread("dunnart_enhancer_annotation.txt")
+enhancer.E10 <- fread("E10.5_enhancer_annotation.txt")
+enhancer.E11<- fread("E11.5_enhancer_annotation.txt")
+enhancer.E12<- fread("E12.5_enhancer_annotation.txt")
+enhancer.E13<- fread("E13.5_enhancer_annotation.txt")
+enhancer.E14<- fread("E14.5_enhancer_annotation.txt")
+enhancer.E15<- fread("E15.5_enhancer_annotation.txt")
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/consensus/promoters/")
+promoters <- fread("dunnart_promoter_annotation.txt")
+promoter.E10 <- fread("E10.5_promoter_annotation.txt")
+promoter.E11<- fread("E11.5_promoter_annotation.txt")
+promoter.E12<- fread("E12.5_promoter_annotation.txt")
+promoter.E13<- fread("E13.5_promoter_annotation.txt")
+promoter.E14<- fread("E14.5_promoter_annotation.txt")
+promoter.E15<- fread("E15.5_promoter_annotation.txt")
+
+enhancers$cre <- "enhancer"
+promoters$cre <- "promoter"
+
+promoter.E10$cre <- "promoter"
+promoter.E11$cre <- "promoter"
+promoter.E12$cre <- "promoter"
+promoter.E13$cre <- "promoter"
+promoter.E14$cre <- "promoter"
+promoter.E15$cre <- "promoter"
+
+enhancer.E10$cre <- "enhancer"
+enhancer.E11$cre <- "enhancer"
+enhancer.E12$cre <- "enhancer"
+enhancer.E13$cre <- "enhancer"
+enhancer.E14$cre <- "enhancer"
+enhancer.E15$cre <- "enhancer"
+
+enhancers$stage <- "P0"
+promoters$stage <- "P0"
+
+promoter.E10$stage <- "E10.5"
+promoter.E11$stage <- "E11.5"
+promoter.E12$stage <- "E12.5"
+promoter.E13$stage <- "E13.5"
+promoter.E14$stage <- "E14.5"
+promoter.E15$stage <- "E15.5"
+
+enhancer.E10$stage <- "E10.5"
+enhancer.E11$stage <- "E11.5"
+enhancer.E12$stage <- "E12.5"
+enhancer.E13$stage <- "E13.5"
+enhancer.E14$stage <- "E14.5"
+enhancer.E15$stage <- "E15.5"
+
+enhancer <- rbind(enhancers, enhancer.E10,enhancer.E11, enhancer.E12, enhancer.E13, enhancer.E14, enhancer.E15)
+promoter <- rbind(promoters, promoter.E10, promoter.E11, promoter.E12, promoter.E13, promoter.E14, promoter.E15)
+enhancer = enhancer[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+promoter = promoter[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+
+
+pdf("test2.pdf", width=10, height = 7)
+ggplot(promoter, aes(x=log10_abs_dist, color = stage, fill=stage)) + geom_density(alpha=0.3) + scale_y_continuous(labels = scales::percent) + scale_x_continuous(limits=c(-7, 7))  + theme_bw()
+dev.off()
+
++ scale_color_manual(values = c("#E9C46A","#264653","#2A9D8F")) + scale_fill_manual(values = c("#E9C46A","#264653","#2A9D8F"))
+
+enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
+promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
+enhancer$cre <- "enhancer"
+promoter$cre <- "promoter"
+peak <- rbind(promoter, enhancer) %>% as.data.table()
+peak = peak[, distanceToTSS.abs:= abs(distanceToTSS)]
+# peak = peak[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+# enhancer = enhancer[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+# promoter = promoter[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
+# peak = peak[,log10_intensity:=log10(V7)]
+# peak = peak[,log10_length:=log10(width)]
+
+pdf("test.pdf", width=10, height = 7)
+ggplot(promoter, aes(x=log10_abs_dist)) + geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + scale_x_continuous(limits=c(-7, 7)) + theme_bw()
+dev.off()
+
+pdf("test.pdf", width=10, height = 7)
+ggplot(enhancer, aes(x=log10_abs_dist, y=log10_length)) + geom_density(alpha=0.5, color="#E9C46A", fill="#E9C46A") + scale_y_continuous(limits=c(0,0.3), labels = scales::percent) + scale_x_continuous(limits=c(-7, 7)) + theme_bw()
+dev.off()
+
+ggplot(peak, aes(abs(distanceToTSS), y=log10_intensity, color=cre)) + geom_point(alpha=0.3) +  theme_bw() + scale_color_manual(values=c("#F1DAA2", "#7AC2B9"))
+dev.off()
+# peak length vs distance from TSS and peak intensity vs distance from TSS
+pdf("peakIntensityVsDist.pdf", width=10, height = 7)
+p <- ggscatter(
+  peak, x = "distanceToTSS", y = "width",
+  color = "cre", palette = "jco", add="reg.line",
+  conf.int = TRUE, # Add confidence interval
+  cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
+  cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"), xlab = "Number of genes per scaffold",
+  ylab = "Number of peaks per scaffold"
+  ) +
+  facet_wrap(~cre)
+p + theme(panel.spacing = unit(2, "lines"), plot.margin = margin(2, 2, 2, 2, "cm"))
+dev.off()
+
+pdf("peakLengthVsDist.pdf", width=10, height = 7)
+ggplot(peak, aes(abs(distanceToTSS), y=log10_length, color=cre)) + geom_point(alpha=0.3) +  theme_bw() + scale_color_manual(values=c("#F1DAA2", "#7AC2B9"))
+dev.off()
+
+
+
+## Peak intensity for distal enhancers marked by H3K4me3
+# subset log10_abs_dist between 3.5 and 7 (3000bp and 10 million bps)
+enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
+promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
+enhancer$cre <- "enhancer"
+promoter$cre <- "promoter"
+enhancer = enhancer[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
+promoter = promoter[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
+
+promoter.subset=subset(promoter, log10_abs_dist >= 3.5)
+promoter = subset(promoter, log10_abs_dist <3.5)
+promoter.subset$cre = "subset"
+data = rbind(promoter, promoter.subset)
+data = data[,log10_intensity:=log10(V7)]
+
+p <- ggplot(data, aes(factor(cre), y = log10_intensity)) + 
+  #geom_violin(aes(fill=factor(cre), color=factor(cre)),
+   # position = "dodge"
+   # )+
+  geom_boxplot(aes(color=factor(cre)),
+    outlier.shape = NA,
+    width = .15, 
+    notch = TRUE,
+    fill=c("#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  ) + theme_bw() + xlab("") + ylab("Peak intensity") 
+pdf("distalH3K4me3PeakIntensity.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" )
+dev.off()
+
+res <- wilcox.test(V7 ~ cre, data = data,
+                   exact = FALSE)
+# number of peaks per gene
+enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
+promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
+enhancer$cre <- "enhancer"
+promoter$cre <- "promoter"
+peak <- rbind(enhancer, promoter)
+peak.counts <- peak %>% as.data.frame() %>% group_by(geneId, cre) %>%
+    summarise(count = n(), .groups = 'drop') %>% as.data.table()
+peak.counts = peak.counts[,log10_abs_dist:=log10(count)][,log10_abs_dist:=ifelse(count<0,-log10_abs_dist,log10_abs_dist)]
+pick <- peak.counts %>% filter(count > 50)
+
+p <- ggplot(peak.counts, aes(factor(cre), y = log10_abs_dist)) + 
+  geom_violin(aes(fill=factor(cre), color=factor(cre))) +
+  geom_boxplot(aes(color=factor(cre)),
+    width = .15, 
+    outlier.shape = NA,
+    fill=c("#FCF8EC","#E4F3F1"),
+    position = position_dodge(width=.1)
+  )  +
+  geom_text(data = pick, aes(factor(cre), log10_abs_dist), label = pick$geneId, check_overlap = TRUE, size=3, position = position_jitter(w = 0.3)) +
+  theme_bw() + xlab("") + ylab("Log 10 number of peaks") 
+pdf("numberPeaksPerGene.pdf", width=9, height = 8)
+p +  scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
+scale_fill_manual(values = c("#F1DAA2","#7AC2B9"))
+dev.off()
+
+## gene length and number of peaks per gene
+p <- ggplot(peak.counts, aes(x=log10_abs_length, y = log10(n))) + 
+  geom_point()+ theme_bw() + xlab("Gene Length") + ylab("Log 10 number of peaks") 
+pdf("numberPeaksvsGeneLength.pdf", width=9, height = 8)
+p
+dev.off()
+
+## Motif Enrichment 
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/motifEnrichment/enhancers/")
+enhancers <- read.csv("knownResults.txt", sep=",",header=TRUE, quote="")
+enhancers <- enhancers[order(enhancers$Log_P.value),] %>% head(17)
+enhancers <- enhancers[-c(8,14), ]  
+enhancers <- enhancers %>% dplyr::select(Motif_Name, Log_P.value)
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/dunnart/results/macs2/motifEnrichment/promoters/")
+promoters <- read.csv("knownResults.txt", sep="\t")
+promoters <- head(promoters, 15)
+promoters <- promoters %>% dplyr::select(Motif.Name, Log.P.value)
+
+matrix <- data.matrix(abs(promoters$Log.P.value))
+rownames(matrix) = c('Nrf2','Bach1','NFE2L2','NF-E2','YY1','Bach2','Jun-AP1', 'Fosl2', 'LEP', 'MafK', 'Fra2', 'ETS', 'EKLF', 'Zfp281', 'E-box')
+
+pdf('promoter_motifEnrichment.pdf',width=3,height = 7)
+Heatmap(matrix, col=c('#e4f3f1', '#cae7e3', '#afdad5', '#95cec7', '#7ac2b9', '#5fb6ab', '#45a99d', '#2a9d8f'))
+dev.off()
+col=c('#e4f3f1', '#cae7e3', '#afdad5', '#95cec7', '#7ac2b9', '#5fb6ab', '#45a99d', '#2a9d8f')
+
+col=c('#fcf8ec', '#f9f0da','#f7e9c7','#f4e2b4','#f1daa2','#eed38f','#eccb7d','#e9c46a')
+
+
+
+
+
+
diff --git a/peak_QC.R b/peak_QC.R
new file mode 100644
index 0000000000000000000000000000000000000000..7a0cdef7dade734d259a5bd3e3c4af2dd4b13cb5
--- /dev/null
+++ b/peak_QC.R
@@ -0,0 +1,302 @@
+## Input: ChIP-seq peak files
+## Output: Various QC plots looking at number of peaks, length, peak intensity etc.
+
+library(ChIPpeakAnno)
+library(data.table)
+library(ggplot2)
+
+###################################################################################################
+############################## QUALITY CONTROL ON PEAKS ###########################################
+###################################################################################################
+
+## Number of overlapping peaks for histone marks
+peak1 <- toGRanges("H3K27ac_overlap.narrowPeak")
+peak2 <- toGRanges("H3K4me3_overlap.narrowPeak")
+makeVennDiagram(list(peak1, peak2), NameOfPeaks=c("H3K27ac", "H3K4me3"), 
+                maxgap=-1L, minoverlap=0L,
+                totalTest=150000,scaled=FALSE, euler.d=FALSE, 
+                fill=c("#6699CC", "#CC6677")) # circle fill color
+
+setwd("/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/5-mousePeaks/2-Subsample10Mreads/")
+
+pk1<- toGRanges("H3K4me3_E10.5_overlap.narrowPeak")
+pk2<- toGRanges("H3K4me3_E11.5_overlap.narrowPeak")
+pk3<- toGRanges("H3K4me3_E12.5_overlap.narrowPeak")
+pk4<- toGRanges("H3K4me3_E13.5_overlap.narrowPeak")
+pk5<- toGRanges("H3K4me3_E14.5_overlap.narrowPeak")
+pk6<- toGRanges("H3K4me3_E15.5_overlap.narrowPeak")
+makeVennDiagram(list(pk6,pk12), NameOfPeaks=c("H3K4me3", "H3K27ac"), 
+                maxgap=-1L, minoverlap=0L,
+                totalTest=150000,scaled=FALSE, euler.d=FALSE,
+                fill=c("#6699CC", "#CC6677"))
+
+pk7<- toGRanges("H3K27ac_E10.5_overlap.narrowPeak")
+pk8<- toGRanges("H3K27ac_E11.5_overlap.narrowPeak")
+pk9<- toGRanges("H3K27ac_E12.5_overlap.narrowPeak")
+pk10<- toGRanges("H3K27ac_E13.5_overlap.narrowPeak")
+pk11<- toGRanges("H3K27ac_E14.5_overlap.narrowPeak")
+pk12<- toGRanges("H3K27ac_E15.5_overlap.narrowPeak")
+
+
+setwd("/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/2-CrossSpecies/4-Peaks/2-DunnartSubsample10Mreads/")
+dH3K4me3 <- read.table("H3K4me3_overlap.narrowPeak", header=FALSE)
+dH3K27ac <- read.table("H3K27ac_overlap.narrowPeak", header=FALSE)
+denhancer <- read.table("dunnart_enhancer_peaks.narrowPeak", header=FALSE)
+dpromoter <- read.table("dunnart_promoter_peaks.narrowPeak", header=FALSE)
+
+setwd("/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/2-CrossSpecies/4-Peaks/1-MouseSubsample10Mreads/")
+
+mH3K4E10 <- read.table("H3K4me3_E10.5_overlap.narrowPeak", header=FALSE)
+mH3K4E11 <- read.table("H3K4me3_E11.5_overlap.narrowPeak", header=FALSE)
+mH3K4E12 <- read.table("H3K4me3_E12.5_overlap.narrowPeak", header=FALSE)
+mH3K4E13 <- read.table("H3K4me3_E13.5_overlap.narrowPeak", header=FALSE)
+mH3K4E14 <- read.table("H3K4me3_E14.5_overlap.narrowPeak", header=FALSE)
+mH3K4E15 <- read.table("H3K4me3_E15.5_overlap.narrowPeak", header=FALSE)
+
+mH3K27E10 <- read.table("H3K27ac_E10.5_overlap.narrowPeak", header=FALSE)
+mH3K27E11 <- read.table("H3K27ac_E11.5_overlap.narrowPeak", header=FALSE)
+mH3K27E12 <- read.table("H3K27ac_E12.5_overlap.narrowPeak", header=FALSE)
+mH3K27E13 <- read.table("H3K27ac_E13.5_overlap.narrowPeak", header=FALSE)
+mH3K27E14 <- read.table("H3K27ac_E14.5_overlap.narrowPeak", header=FALSE)
+mH3K27E15 <- read.table("H3K27ac_E15.5_overlap.narrowPeak", header=FALSE)
+
+enhancer10 <- read.table("E10.5_enhancer_peaks.narrowPeak", header=FALSE)
+enhancer11 <- read.table("E11.5_enhancer_peaks.narrowPeak", header=FALSE)
+enhancer12 <- read.table("E12.5_enhancer_peaks.narrowPeak", header=FALSE)
+enhancer13 <- read.table("E13.5_enhancer_peaks.narrowPeak", header=FALSE)
+enhancer14 <- read.table("E14.5_enhancer_peaks.narrowPeak", header=FALSE)
+enhancer15 <- read.table("E15.5_enhancer_peaks.narrowPeak", header=FALSE)
+
+promoter10 <- read.table("E10.5_promoter_peaks.narrowPeak", header=FALSE)
+promoter11 <- read.table("E11.5_promoter_peaks.narrowPeak", header=FALSE)
+promoter12 <- read.table("E12.5_promoter_peaks.narrowPeak", header=FALSE)
+promoter13 <- read.table("E13.5_promoter_peaks.narrowPeak", header=FALSE)
+promoter14 <- read.table("E14.5_promoter_peaks.narrowPeak", header=FALSE)
+promoter15 <- read.table("E15.5_promoter_peaks.narrowPeak", header=FALSE)
+
+
+denhancer$sample <- "enhancer P0"
+dpromoter$sample <- "promoter P0"
+
+enhancer10$sample <- "enhancer E10.5"
+enhancer11$sample <- "enhancer E11.5"
+enhancer12$sample <- "enhancer E12.5"
+enhancer13$sample <- "enhancer E13.5"
+enhancer14$sample <- "enhancer E14.5"
+enhancer15$sample <- "enhancer E15.5"
+
+promoter10$sample <- "promoter E10.5"
+promoter11$sample <- "promoter E11.5"
+promoter12$sample <- "promoter E12.5"
+promoter13$sample <- "promoter E13.5"
+promoter14$sample <- "promoter E14.5"
+promoter15$sample <- "promoter E15.5"
+
+enhancer10$species <- "mouse"
+enhancer11$species <- "mouse"
+enhancer12$species <- "mouse"
+enhancer13$species <- "mouse"
+enhancer14$species <- "mouse"
+enhancer15$species <- "mouse"
+
+promoter10$species <- "mouse"
+promoter11$species <- "mouse"
+promoter12$species <- "mouse"
+promoter13$species <- "mouse"
+promoter14$species <- "mouse"
+promoter15$species <- "mouse"
+
+denhancer$species <- "dunnart"
+dpromoter$species <- "dunnart"
+
+enh_prom <- rbind(denhancer, dpromoter, enhancer10, enhancer11, enhancer12, enhancer13, enhancer14, enhancer15, promoter10, promoter11, promoter12, promoter13, promoter14, promoter15 )
+
+dH3K4me3$sample <- "H3K4me3 P0"
+dH3K27ac$sample <- "H3K27ac P0"
+dH3K4me3$species <- "dunnart"
+dH3K27ac$species <- "dunnart"
+
+
+mH3K4E10$species <- "mouse"
+mH3K4E11$species <- "mouse"
+mH3K4E12$species <- "mouse"
+mH3K4E13$species <- "mouse"
+mH3K4E14$species <- "mouse"
+mH3K4E15$species <- "mouse"
+
+mH3K4E10$sample <- "H3K4me3 E10.5"
+mH3K4E11$sample <- "H3K4me3 E11.5"
+mH3K4E12$sample <- "H3K4me3 E12.5"
+mH3K4E13$sample <- "H3K4me3 E13.5"
+mH3K4E14$sample <- "H3K4me3 E14.5"
+mH3K4E15$sample <- "H3K4me3 E15.5"
+
+mH3K27E10$species <- "mouse"
+mH3K27E11$species <- "mouse"
+mH3K27E12$species <- "mouse"
+mH3K27E13$species <- "mouse"
+mH3K27E14$species <- "mouse"
+mH3K27E15$species <- "mouse"
+
+mH3K27E10$sample <- "H3K27ac E10.5"
+mH3K27E11$sample <- "H3K27ac E11.5"
+mH3K27E12$sample <- "H3K27ac E12.5"
+mH3K27E13$sample <- "H3K27ac E13.5"
+mH3K27E14$sample <- "H3K27ac E14.5"
+mH3K27E15$sample <- "H3K27ac E15.5"
+
+fullPeaks <- rbind(dH3K4me3, dH3K27ac, mH3K27E10, mH3K27E11, mH3K27E12, mH3K27E13, mH3K27E14, mH3K27E15, mH3K4E10, mH3K4E11, mH3K4E12, mH3K4E13, mH3K4E14, mH3K4E15)
+
+# Number of peaks per gene for enhancers and promoters
+setwd("/Users/lauracook/OneDrive - The University of Melbourne/PhD (2018-2021)/6-ChIPseq/3-DataAnalysis/4-dunnartPeaks/1-FullDataset/")
+geneCounts <- read.table("geneCounts.txt", header=TRUE)
+
+ggplot(geneCounts, aes(element, freq, fill = element)) +     
+  geom_boxplot() + theme(legend.position="none") + theme_bw() + xlab("") + ylab("Number of peaks per gene") + theme(legend.position="none") + scale_fill_manual(values = c("#199480", "#99cfc6") )
+
+# Top genes with high number of peaks (enhancers and promoters)
+sortGeneCounts <- geneCounts[order(-geneCounts$freq),]
+
+
+# Number of consensus peaks for H3K4me3 and H3K27ac
+tbl <- with(fullPeaks, table(sample, sample))
+
+ggplot(as.data.frame(tbl), aes(factor(sample), Freq, fill = sample)) +     
+  geom_col(position = 'dodge') + theme(legend.position="none")  + theme_bw() + xlab("") + 
+  ylab("Number of peaks") + theme(axis.ticks.x=element_blank(), legend.position="none") + 
+  scale_y_continuous(expand = c(0, 0)) + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
+
+# Number of consensus peaks for combined peaks
+tbl <- with(enh_prom, table(sample, sample))
+
+ggplot(as.data.frame(tbl), aes(factor(sample), Freq, fill = sample)) +     
+  geom_col(position = 'stack') + theme(legend.position="none") + theme_bw() + 
+  xlab("") + ylab("Number of peaks") + theme(axis.ticks.x=element_blank(), 
+                                             axis.line.x=element_blank(), 
+                                             legend.position="none") + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1)) + 
+  scale_y_continuous(expand = c(0, 0))
+
+# Peak lengths for H3K4me3 and H3K27ac
+fullPeaks$length <- (fullPeaks$V3 - fullPeaks$V2)
+fullPeaks$lengthkb <- fullPeaks$length / 1000
+
+ggplot(fullPeaks, aes(sample, log10(lengthkb), fill = sample)) +     
+  geom_boxplot() + theme(legend.position="none") + theme_bw() + xlab("") + 
+  ylab("Log 10 peak length (kb)") + theme(axis.ticks.x=element_blank(), legend.position="none") + scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
+
+# Peak lengths for enhancers and promoters
+enh_prom$length <- (enh_prom$V3 - enh_prom$V2)
+enh_prom$lengthkb <- enh_prom$length / 1000
+
+ggplot(enh_prom, aes(sample, log10(lengthkb), fill = sample)) +     
+  geom_boxplot() + theme(legend.position="none") + 
+  theme_bw() + xlab("") + ylab("Log 10 peak length (kb)") + 
+  theme(legend.position="none") + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1)) + 
+  scale_y_continuous(expand = c(0, 0))
+
+# Peak intensity (fold-change) for enhancer/promoters and full peaks
+ggplot(fullPeaks, aes(sample, V7, fill = sample)) +     
+  geom_boxplot() + theme(legend.position="none") + theme_bw() + xlab("") + ylab("Peak intensity (fold-change)") + theme(legend.position="none")  + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                                 "#DDDDDD", "#DDDDDD",
+                                 "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                                 "#FFAABB", "#FFAABB", 
+                                 "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
+
+
+ggplot(enh_prom, aes(sample, V7, fill = sample)) +     
+  geom_boxplot() + theme(legend.position="none") + theme_bw() + xlab("") + ylab("Peak intensity (fold-change)") + theme(legend.position="none") + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#FFAABB", "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB", 
+                               "#FFAABB", "#FFAABB")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
+
+  ### Filter top 20,000 peaks in dunnart and see peak intensities
+  H3K4me3top <- data.table(dH3K4me3[order(-dH3K4me3$V7), ][1:20000,])
+  H3K27actop <- data.table(dH3K27ac[order(-dH3K27ac$V7), ][1:30000,])
+  fullPeaks <- rbind(H3K4me3top, H3K27actop, mH3K27E10, mH3K27E11, mH3K27E12, mH3K27E13, mH3K27E14, mH3K27E15, mH3K4E10, mH3K4E11, mH3K4E12, mH3K4E13, mH3K4E14, mH3K4E15)
+  
+  enhTop <- data.table(denhancer[order(-denhancer$V7), ][1:20000,])
+  promTop <- data.table(dpromoter[order(-dpromoter$V7), ][1:20000,])
+  enh_prom <- rbind(enhTop, promTop, enhancer10, enhancer11, enhancer12, enhancer13, enhancer14, enhancer15, promoter10, promoter11, promoter12, promoter13, promoter14, promoter15 )
+  
+  
+###################################################################################################
+#################################### BINNING PEAKS ################################################
+###################################################################################################
+
+## Convert peak lengths to a data table
+data_table <- data.table(enh_prom[ which(enh_prom$species=='mouse'
+                         & enh_prom$sample=='promoter E10.5'), ])
+
+
+## Bin peaks into different widths
+data_table = data_table[
+  ,binned_column:= ifelse(length <= 1001, '0-1000',
+                          ifelse(length > 1001 & length <=2001,'1001-2000',
+                                 ifelse(length > 2001 & length <=3001,'201-3000',
+                                        ifelse(length > 3001 & length <=4001,'3001-4000',
+                                               ifelse(length > 4001 & length <=5001,'4001-5000',
+                                                      ifelse(length > 5001 & length <=6001,'5001-6000',
+                                                             ifelse(length > 6001 & length <=7001,'6001-7000',
+                                                                    ifelse(length > 7001 & length <=8001,'7001-8000',
+                                                                           ifelse(length > 8001 & length <=9001,'8001-9000',
+                                                                                  ifelse(length > 9001 & length <=10001,'901-10000',
+                                                                                         ifelse(length > 10001 & length <=11001,'10001-11000',
+                                                                                                ifelse(length > 11001 & length <=12001,'11001-12000',
+                                                                                                       ifelse(length > 12001 & length <=13001,'12001-13000',
+                                                                                                              ifelse(length > 13001 & length <=14001,'13001-14000',
+                                                                                                                     ifelse(length > 14001 & length <=15001,'14001-15000',
+                                                                                                                            ifelse(length > 15001 & length <=16001,'15001-16000',
+                                                                                                                                   ifelse(length > 16000, '>16000', 'NA'
+                                 )))))))))))))))))
+]
+
+
+level_order <-c("0-1000", "1001-2000", 
+                  "2001-3000", "3001-4000", 
+                "4001-5000", "5001-6000", 
+                "6001-7000", "7001-8000", 
+                "8001-9000", "9001-10000", 
+                "10001-11000", "11001-12000",
+                "12001-13000", "13001-14000",
+                "14001-15000", "15001-16000",
+                ">16000")
+
+tbl <- with(data_table, table(binned_column, binned_column))
+
+ggplot(as.data.frame(tbl), aes(factor(binned_column, levels=level_order), Freq, fill = binned_column)) +
+  geom_col(position = 'dodge') + theme(legend.position="none")  + theme_bw() + xlab("") + ggtitle("mouse E10.5 promoters")+
+  ylab("Number of peaks") + theme(axis.ticks.x=element_blank(), legend.position="none")+
+  scale_y_continuous(expand = c(0, 0)) + 
+  scale_fill_manual(values = c("#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD",
+                               "#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", 
+                               "#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD", "#DDDDDD")) + 
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
diff --git a/peak_metrics.R b/peak_metrics.R
new file mode 100644
index 0000000000000000000000000000000000000000..aea96665296b41290bf7f691907d0f1a195937a0
--- /dev/null
+++ b/peak_metrics.R
@@ -0,0 +1,109 @@
+## Input: annotation files from annotation_10M.R 
+## Output: peak metrics file for ALL peak files (mouse and dunnart)
+## Can be used as input for downstream analyses. eg. PCA
+
+library(dplyr)
+library(tidyverse)
+library(data.table)
+
+setwd("/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation/")
+
+peakData <- function(fileList, plot1, plot2, plot3, outFile, colnames){
+  files =list.files(fileList, pattern= ".txt", full.names=T) # create list of files in directory
+  files = as.list(files)
+  data = lapply(files, function(x) read.table(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
+  #data = lapply(data, function(x){ x$peakLength <- x$end - x$start;return(x)})
+  data = lapply(data, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
+  subset = lapply(data, function(x) x %>% dplyr::select(seqnames, start, end, width, V7, mouseensembl) %>% as.data.table()) #V7 or V5
+  
+  # split = lapply(subset, function(x) x %>% split(by="mouseensembl")) ## split by ENSEMBL ID
+  # split = lapply(split, function(x) lapply(x, function(i) i=setorderv(i,c('start', 'end'),1))) ## order peaks on start stop coordinates
+  # split = lapply(split, function(x) lapply(x, function(i) i=i[,distanceBetweenPeaks:=lead(start)-end])) ## find distance between peaks with the same nearest gene
+  # df = lapply(split, function(x) rbindlist(x)) ## bind rows back together
+  df = subset
+  names(df) = c("dunnart", "E10.5", "E11.5", "E12.5", "E13.5", "E14.5", "E15.5") ## add names to the dataframes for plots
+  
+  # pdf(plot1) # plot peak length versus peak intensity
+  # plot_lst = lapply(seq_along(df), function(i) 
+  #           print(ggplot(df[[i]], aes(x=width, y=V5)) + 
+  #           geom_point() + 
+  #           theme(plot.title = element_text(hjust = 0.5)) +
+  #           ggtitle(names(df)[i])))
+  # dev.off()
+
+  #df2 = lapply(df, function(x) x[complete.cases(x[ , 7]),]) ## remove rows with NAs in the distance between between column
+  
+
+  # pdf(plot2) # plot distribution of distances between peaks per gene
+  # plot_lst = lapply(seq_along(df2), function(i) 
+  #            print(ggplot(df2[[i]], aes(x=distanceBetweenPeaks)) + scale_x_continuous(trans="log10", name="Log10 distance between peaks") +
+  #             geom_density() + ylim(c(0,0.6)) +
+  #            theme(plot.title = element_text(hjust = 0.5)) +
+  #            ggtitle(names(df2)[i])))
+  # dev.off()
+
+  df = lapply(df, function(x) transform(x, intensity.divBy.length = V7 / width))
+  df = lapply(df, function(x) add_tally(x))
+  # pdf(plot3) # plot distribution of peak intensity divided by length
+  # plot_lst = lapply(seq_along(df), function(i) 
+  #           print(ggplot(df[[i]], aes(x=intensity.divBy.length)) + #+ scale_x_continuous(trans="log10", name="density") +
+  #           geom_density() + 
+  #           theme(plot.title = element_text(hjust = 0.5)) +
+  #           ggtitle(names(df)[i])))
+  # dev.off()
+
+  df = lapply(df, function(x) x %>% dplyr::select(mouseensembl, n, V7, width, intensity.divBy.length) %>% as.data.table())
+  metrics = lapply(df, function(x) as.data.frame(x) %>% group_by(mouseensembl, n) %>%
+    summarise(count = n(), peakLength.Sum = sum(width), intensitySum = sum(V7), intensityMedian = median(V7), intensityByLength.Median = median(intensity.divBy.length), 
+    intensityMean = mean(V7), intensityByLength.Mean = mean(V7), .groups = 'drop') %>% as.data.table())
+  metrics = lapply(metrics, function(x) transform(x, peakFraction = (count / n)*100) )
+  merged <- metrics %>% purrr::reduce(full_join, by = "mouseensembl")  # join all dataframes by ensembl geneID
+  colnames(merged) = colnames # update colnames
+  merged[is.na(merged)]= 0
+  write.table(merged, file=outFile, sep="\t", quote=F, row.names=F) 
+}
+
+# Import annotated peak files (for the dunnart this is the output file that has converted IDs to mouse ensembl)
+peakData(fileList = "consensus/promoters/clustered/", colnames = c("geneId", "smiCra1.totalPeaks", "smiCra1.peak.count", "smiCra.peakLength.Sum", "smiCra.intensity.Sum",
+           "smiCra1.intensity.Median", "smiCra1.intensityByLength.Median", "smiCra1.intensityMean", 
+            "smiCra1.intensityByLength.Mean","smiCra1.peak.fraction", "mm10.E10.5.totalPeaks", "mm10.E10.5.peak.count", "mm10.E10.5.peakLength.Sum", "mm10.E10.5.intensity.Sum",
+           "mm10.E10.5.intensity.Median",  "mm10.E10.5.intensityByLength.Median", "mm10.E10.5.intensityMean",
+            "mm10.E10.5.intensityByLength.Mean", "mm10.E10.5.peak.fraction", "mm10.E11.5.totalPeaks",
+            "mm10.E11.5.peak.count", "mm10.E11.5.peakLength.Sum", "mm10.E11.5.intensity.Sum",
+           "mm10.E11.5.intensity.Median",  "mm10.E11.5.intensityByLength.Median", "mm10.E11.5.intensityMean",
+            "mm10.E11.5.intensityByLength.Mean", "mm10.E11.5.peak.fraction",  "mm10.E12.5.totalPeaks",
+            "mm10.E12.5.peak.count", "mm10.E12.5.peakLength.Sum", "mm10.E12.5.intensity.Sum",
+           "mm10.E12.5.intensity.Median", "mm10.E12.5.intensityByLength.Median",  "mm10.E12.5.intensityMean",
+            "mm10.E12.5.intensityByLength.Mean", "mm10.E12.5.peak.fraction", "mm10.E13.5.totalPeaks",
+            "mm10.E13.5.peak.count", "mm10.E13.5.peakLength.Sum", "mm10.E13.5.intensity.Sum",
+           "mm10.E13.5.intensity.Median",  "mm10.E13.5.intensityByLength.Median", "mm10.E13.5.intensityMean",
+            "mm10.E13.5.intensityByLength.Mean",  "mm10.E13.5.peak.fraction", "mm10.E14.5.totalPeaks",
+            "mm10.E14.5.peak.count", "mm10.E14.5.peakLength.Sum", "mm10.E14.5.intensity.Sum",
+           "mm10.E14.5.intensity.Median", "mm10.E14.5.intensityByLength.Median",  "mm10.E14.5.intensityMean",
+            "mm10.E14.5.intensityByLength.Mean", "mm10.E14.5.peak.fraction",  "mm10.E15.5.totalPeaks",
+            "mm10.E15.5.peak.count", "mm10.E15.5.peakLength.Sum", "mm10.E15.5.intensity.Sum",
+           "mm10.E15.5.intensity.Median",  "mm10.E15.5.intensityByLength.Median", "mm10.E15.5.intensityMean",
+            "mm10.E15.5.intensityByLength.Mean", "mm10.E15.5.peak.fraction"), plot1="promoterPeakIntensityVsLength_clustered.pdf", plot2="promoterDistanceBetweenPeaksDensity_clustered.pdf",
+            plot3="promoterPeakIntensityByLengthDensity_clustered.pdf", outFile = "promoterPeaksPerGene_clustered.txt")
+
+peakData(fileList = "consensus/enhancers/annotation/", colnames = c("geneId", "smiCra1.totalPeaks", "smiCra1.peak.count", "smiCra.peakLength.Sum", "smiCra.intensity.Sum",
+           "smiCra1.intensity.Median", "smiCra1.intensityByLength.Median", "smiCra1.intensityMean", 
+            "smiCra1.intensityByLength.Mean","smiCra1.peak.fraction", "mm10.E10.5.totalPeaks", "mm10.E10.5.peak.count", "mm10.E10.5.peakLength.Sum", "mm10.E10.5.intensity.Sum",
+           "mm10.E10.5.intensity.Median",  "mm10.E10.5.intensityByLength.Median", "mm10.E10.5.intensityMean",
+            "mm10.E10.5.intensityByLength.Mean", "mm10.E10.5.peak.fraction", "mm10.E11.5.totalPeaks",
+            "mm10.E11.5.peak.count", "mm10.E11.5.peakLength.Sum", "mm10.E11.5.intensity.Sum",
+           "mm10.E11.5.intensity.Median",  "mm10.E11.5.intensityByLength.Median", "mm10.E11.5.intensityMean",
+            "mm10.E11.5.intensityByLength.Mean", "mm10.E11.5.peak.fraction",  "mm10.E12.5.totalPeaks",
+            "mm10.E12.5.peak.count", "mm10.E12.5.peakLength.Sum", "mm10.E12.5.intensity.Sum",
+           "mm10.E12.5.intensity.Median", "mm10.E12.5.intensityByLength.Median",  "mm10.E12.5.intensityMean",
+            "mm10.E12.5.intensityByLength.Mean", "mm10.E12.5.peak.fraction", "mm10.E13.5.totalPeaks",
+            "mm10.E13.5.peak.count", "mm10.E13.5.peakLength.Sum", "mm10.E13.5.intensity.Sum",
+           "mm10.E13.5.intensity.Median",  "mm10.E13.5.intensityByLength.Median", "mm10.E13.5.intensityMean",
+            "mm10.E13.5.intensityByLength.Mean",  "mm10.E13.5.peak.fraction", "mm10.E14.5.totalPeaks",
+            "mm10.E14.5.peak.count", "mm10.E14.5.peakLength.Sum", "mm10.E14.5.intensity.Sum",
+           "mm10.E14.5.intensity.Median", "mm10.E14.5.intensityByLength.Median",  "mm10.E14.5.intensityMean",
+            "mm10.E14.5.intensityByLength.Mean", "mm10.E14.5.peak.fraction",  "mm10.E15.5.totalPeaks",
+            "mm10.E15.5.peak.count", "mm10.E15.5.peakLength.Sum", "mm10.E15.5.intensity.Sum",
+           "mm10.E15.5.intensity.Median",  "mm10.E15.5.intensityByLength.Median", "mm10.E15.5.intensityMean",
+            "mm10.E15.5.intensityByLength.Mean", "mm10.E15.5.peak.fraction"), plot1="enhancerPeakIntensityVsLength_subsample.pdf", plot2="enhancerDistanceBetweenPeaksDensity_subsample.pdf",
+            plot3="enhancerPeakIntensityByLengthDensity_subsample.pdf", outFile = "enhancerPeaksPerGene_subsample.txt")
diff --git a/peak_metrics_PCA.R b/peak_metrics_PCA.R
new file mode 100644
index 0000000000000000000000000000000000000000..012d41f4d3fc3c60bc9b571ff730658ef0a26710
--- /dev/null
+++ b/peak_metrics_PCA.R
@@ -0,0 +1,147 @@
+## Input: peak metrics from peak_metrics.R
+## Output: PCA plots and tables
+
+library(mixOmics)
+library(dplyr)
+
+## PCA on peak metrics
+PCAnalysisALL <- function(file, plot10, plot20, plot30, plot40){
+  file = read.table(file, header=TRUE,  row.names = 1)
+
+  #df =merge(promoter, enhancer,by='row.names',all=TRUE)
+  #rownames(df) = df[,1]
+  #df = df[-1,-1]
+
+  intensitySum <- file %>% dplyr::select(contains("intensity.Sum"))
+  colnames(intensitySum) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  df <- t(intensitySum)
+  df[is.na(df)] <- 0
+  df.pca <- spca(df, scale=TRUE)
+  pdf(plot10)    
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  intensityMean = file %>% dplyr::select(contains("intensityMean"))
+  colnames(intensityMean) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  df <- t(intensityMean)
+  df[is.na(df)] <- 0
+  df.pca <- spca(df, scale=TRUE)
+  pdf(plot20)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  intensity = file %>% dplyr::select(contains("intensity.Median")) 
+  colnames(intensity) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  df <- t(intensity)
+  df[is.na(df)] <- 0
+  df.pca <- spca(df, scale=TRUE)
+  #write.table((selectVar(df.pca, comp = 1)$value), loadingPC1, sep="\t", quote=F, row.names=T)
+  #write.table((selectVar(df.pca, comp = 2)$value), loadingPC2, sep="\t", quote=F, row.names=T)
+  pdf(plot30)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  peak.counts =  file %>% dplyr::select(contains("peak.fraction"))
+  colnames(peak.counts) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  df <- t(peak.counts)
+  df <- df[, colSums(df != 0) > 0]
+  df.pca <- pca(df)
+  pdf(plot40)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+}
+
+setwd('/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation')
+PCAnalysisALL(file = "promoterPeaksPerGene_clustered.txt", plot10 = "promoterintensitySum_clustered.pdf", plot20 ="promoterintensityMean_clustered.pdf", 
+plot30="promoterintensity.Median_clustered.pdf", plot40="promoterPeak.fraction_clustered.pdf")
+
+PCAnalysisALL(file = "enhancerPeaksPerGene_subsample.txt", plot10 = "enhancerIntensitySum_subsample.pdf", plot20 ="enhancerintensityMean_subsample.pdf", 
+plot30="enhancerintensity.Median_subsample.pdf", plot40="enhancerPeak.fraction_subsample.pdf")
+
+PCAnalysismm10<- function(file, plot10, plot20, plot30, plot40){
+  file = read.table(file, header=TRUE,  row.names = 1)
+
+  #df =merge(promoter, enhancer,by='row.names',all=TRUE)
+  #rownames(df) = df[,1]
+  #df = df[-1,-1]
+
+  intensitySum <- file %>% dplyr::select(contains("intensity.Sum"))
+  colnames(intensitySum) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensitySum <- intensitySum %>% dplyr::select("mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensitySum <- intensitySum[apply(intensitySum[,-1], 1, function(x) !all(x==0)),]
+  df <- t(intensitySum)
+  df[is.na(df)] <- 0
+  df.pca <- pca(df, scale=TRUE)
+  pdf(plot10)    
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  intensityMean = file %>% dplyr::select(contains("intensityMean"))
+  colnames(intensityMean) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensityMean <- intensityMean %>% dplyr::select("mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensityMean <- intensityMean[apply(intensityMean[,-1], 1, function(x) !all(x==0)),]
+
+  df <- t(intensityMean)
+  df[is.na(df)] <- 0
+  df.pca <- spca(df, scale=TRUE)
+  pdf(plot20)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  intensity = file %>% dplyr::select(contains("intensity.Median")) 
+  colnames(intensity) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensity <- intensity %>% dplyr::select("mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  intensity<- intensity[apply(intensity[,-1], 1, function(x) !all(x==0)),]
+
+  df <- t(intensity)
+  df[is.na(df)] <- 0
+  df.pca <- spca(df, scale=TRUE)
+  #write.table((selectVar(df.pca, comp = 1)$value), loadingPC1, sep="\t", quote=F, row.names=T)
+  #write.table((selectVar(df.pca, comp = 2)$value), loadingPC2, sep="\t", quote=F, row.names=T)
+  pdf(plot30)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+
+  peak.counts =  file %>% dplyr::select(contains("peak.fraction"))
+  colnames(peak.counts) <- c("dunnart", "mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  peak.counts <- peak.counts %>% dplyr::select("mouse_E10.5", "mouse_E11.5", "mouse_E12.5", "mouse_E13.5", "mouse_E14.5", "mouse_E15.5")
+  peak.counts <- peak.counts[apply(peak.counts[,-1], 1, function(x) !all(x==0)),]
+
+  df <- t(peak.counts)
+  df <- df[, colSums(df != 0) > 0]
+  df.pca <- pca(df)
+  pdf(plot40)
+  plotIndiv(df.pca, scale=T)
+  plot(df.pca)
+  plotLoadings(df.pca)
+  plotLoadings(df.pca, comp=2)
+  dev.off()
+}
+
+setwd('/data/projects/punim0586/lecook/chipseq-pipeline/cross_species/peakAnnotation')
+PCAnalysismm10(file = "promoterPeaksPerGene_clustered.txt", plot10 = "promoterintensitySum_clustered.mm10.pdf", plot20 ="promoterintensityMean_clustered.mm10.pdf", 
+plot30="promoterintensity.Median_clustered.mm10.pdf", plot40="promoterPeak.fraction_clustered.mm10.pdf")
+
+PCAnalysismm10(file = "enhancerPeaksPerGene_subsample.txt", plot10 = "enhancerIntensitySum_subsample.pdf", plot20 ="enhancerintensityMean_subsample.pdf", 
+plot30="enhancerintensity.Median_subsample.pdf", plot40="enhancerPeak.fraction_subsample.pdf")
\ No newline at end of file
diff --git a/subsamplePeaks.R b/subsamplePeaks.R
new file mode 100644
index 0000000000000000000000000000000000000000..0bc2607b7221ed64b917fd57d187cd17eb61d38d
--- /dev/null
+++ b/subsamplePeaks.R
@@ -0,0 +1,22 @@
+library(data.table)
+library(dplyr)
+library(tidyverse)
+
+peakSubsample <- function(x, out){
+    data = read.table(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")) # read in all files
+    data = data.table(data[order(-data$V7), ][1:20000,])
+    write.table(data, file=out, sep="\t", quote=F, row.names=F) 
+}
+
+peakSubsample(x="consensus/enhancers/annotation/dunnart_enhancer_annotationConvertedIDs.txt", 
+out = "consensus/enhancers/annotation/dunnart_enhancer_annotationConvertedIDs_subSample.txt")
+
+peakSubsample(x="consensus/promoters/annotation/dunnart_promoter_annotationConvertedIDs.txt", 
+out = "consensus/promoters/annotation/dunnart_promoter_annotationConvertedIDs_subSample.txt")
+
+
+peakSubsample(x="consensus/promoters/dunnart_promoter_peaks.narrowPeak", 
+out = "consensus/promoters/dunnart_promoter_peaks_subsample.narrowPeak")
+
+peakSubsample(x="consensus/enhancers/dunnart_enhancer_peaks.narrowPeak", 
+out = "consensus/enhancers/dunnart_enhancer_peaks_subsample.narrowPeak")