Commit 6412db16 authored by Research Platforms's avatar Research Platforms

Update for 20 Nov

parent 053e6f17
......@@ -14,5 +14,6 @@ NAMD/apoa1
NAMD/NAMD_BENCHMARKS_SPARTAN
NAMD/stmv
Python/minitwitter.csv
SAMtools/sample.sam.gz
Singularity/vsoch-hello-world-master.simg
#!/bin/bash
# To give your job a name, replace "MyJob" with an appropriate name
#SBATCH --job-name=ABRicate-test.slurm
#SBATCH -p cloud
# Run on single CPU
#SBATCH --ntasks=1
# set your minimum acceptable walltime=days-hours:minutes:seconds
#SBATCH -t 0:15:00
# Specify your email address to be notified of progress.
# SBATCH --mail-user=youreamiladdress@unimelb.edu
# SBATCH --mail-type=ALL
# Load the environment variables
module load ABRicate/0.8.7-spartan_intel-2017.u2
# The command to actually run the job
abricate ecoli_rel606.fasta
This diff is collapsed.
#!/bin/bash
# To give your job a name, replace "MyJob" with an appropriate name
#SBATCH --job-name=ABySS-test.slurm
#SBATCH -p cloud
# Run on single CPU
#SBATCH --ntasks=1
# set your minimum acceptable walltime=days-hours:minutes:seconds
#SBATCH -t 0:15:00
# Specify your email address to be notified of progress.
# SBATCH --mail-user=youreamiladdress@unimelb.edu
# SBATCH --mail-type=ALL
# Load the environment variables
module load ABySS/2.0.2-goolf-2015a
# Assemble a small synthetic data set
tar xzvf test-data.tar.gz
sleep 20
abyss-pe k=25 name=test in='test-data/reads1.fastq test-data/reads2.fastq'
# Calculate assembly contiguity statistics
abyss-fac test-unitigs.fa
#!/bin/bash
# To give your job a name, replace "MyJob" with an appropriate name
#SBATCH --job-name=ADMIXTURE-test.slurm
#SBATCH -p cloud
# Run with two threads
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
# set your minimum acceptable walltime=days-hours:minutes:seconds
#SBATCH -t 0:15:00
# Specify your email address to be notified of progress.
# SBATCH --mail-user=youreamiladdress@unimelb.edu
# SBATCH --mail-type=ALL
# Load the environment variables
module load ADMIXTURE/1.3.0
# Untar sample files, run application
# See admixture --help for options.
tar xvf hapmap3-files.tar.gz
admixture -j2 hapmap3.bed 1
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#!/bin/tcsh
# ---------------------------------------------------------
# if the afni directory is already here, whine a bit and terminate the script
if ( -e ARzs_data/afni ) then
echo ""
echo "Failure:"
echo ""
echo "The resulting afni directory already exists. Please move the"
echo "afni directory and then re-run the script. Example:"
echo ""
echo " mv ARzs_data/afni ARzs_data/afni.bak"
echo ""
exit
endif
# ---------------------------------------------------------
# start in the top level data directory
cd ARzs_data
# ---------------------------------------------------------
# create AFNI dataset from spgr I-files - will move them to afni dir later
to3d -prefix ARzs_spgr -spgr SPGR_data/I.*
# ---------------------------------------------------------
# create AFNI datasets from both EPI runs
cd EPI_data
Ifile -nt '???/I.*'
# change the prefix for the output file in GERT_Reco
sed 's/OutBrick/ARzs_EPI/' GERT_Reco > new_GERT_Reco
# now run the new_GERT_Reco script
tcsh new_GERT_Reco
# -------------------------
# store outliers in a subdir
cd afni
mkdir outliers
mv ARzs_EPI*Outliers.1D outliers
cd ..
# -------------------------
# put the new afni directory back at the top level
mv afni ..
# go back to top level data directory
cd ..
# ---------------------------------------------------------
# put our spgr anat dataset into that new afni directory
mv ARzs_spgr+orig.* afni
# ---------------------------------------------------------
# do volume registering
cd afni
3dvolreg -prefix ARzs_EPI_r1_vr -base ARzs_EPI_r2+orig'[155]' ARzs_EPI_r1+orig
3dvolreg -prefix ARzs_EPI_r2_vr -base ARzs_EPI_r2+orig'[155]' ARzs_EPI_r2+orig
# average the two volume registered time series datasets
3dcalc -a ARzs_EPI_r1_vr+orig -b ARzs_EPI_r2_vr+orig \
-expr '(a+b)/2' -prefix ARzs_EPI_avg
# ---------------------------------------------------------
# create 1D file (Ref_ARzs.1D) containing ideal reference function
waver -GAM -dt 2 -numout 160 \
-inline 15@0 \
15@1 7@0 15@1 8@0 \
15@1 7@0 15@1 8@0 \
15@1 7@0 15@1 8@0 \
10@0 \
> Ref_ARzs.1D
# gasp! actual statistical analysis of data! ...
3ddelay -input ARzs_EPI_avg+orig -ideal_file Ref_ARzs.1D -fs 0.5 -T 45
# ---------------------------------------------------------
# extra stuff
3dIntracranial -anat ARzs_spgr+orig -prefix ARzs_spgr_NoSkl
echo "results are left in directory: ARzs_data/afni"
#!/bin/bash
# To give your job a name, replace "MyJob" with an appropriate name
#SBATCH --job-name=AFNI-test.slurm
#SBATCH -p cloud
# Run on single CPU
#SBATCH --ntasks=1
# set your minimum acceptable walltime=days-hours:minutes:seconds
#SBATCH -t 0:15:00
# Specify your email address to be notified of progress.
# SBATCH --mail-user=youreamiladdress@unimelb.edu
# SBATCH --mail-type=ALL
# Load the environment variables
module load AFNI/linux_openmp_64-spartan_intel-2017.u2-20190219
# Untar dataset and run script
tar xvf ARzs_data.tgz
./@ARzs_analyze
HowTo #1:
Experiment Background
Block design - measurement of time delay to visual stimulus
The Experiment:
In this example, subjects were presented with a checkered, rotating
hemifield. One run consisted of 30 seconds of rest, followed by 6
pairs of 30 seconds ON (rotating hemifield) and 15 seconds OFF (rest)
intervals, and lastly followed by 20 seconds of rest at the end of the
run. Thus each run was 320 seconds long, with a TR of 2 seconds, providing
160 volume images.
The data:
This EPI data consists of 160 volumes, 18 coronal slices each. These
slice images were taken of the back of the brain (28.8 mm posterior to
96.8 mm posterior). Each slice is a 64 by 64 voxel image, with a voxel
resolution of 3.75 mm on each side. The thickness of each of the 18
slices is 4.0 mm.
The SPGR anatomical data consists of 124 axial slices, from 50.6 mm
inferior to 84.7 mm superior. Each slice is a 256 by 256 voxel image,
with each voxel being a 0.938 mm square. The slice thickness (the
distance between 2 slices) is 1.0 mm for the anatomical images.
Note the difference in resolution between the SPGR data and the EPI
data. The EPI data has a much lower resolution. A single voxel of
the EPI data is basically 4 times the size of a single voxel of SPGR
data, along each of the 3 axes. A single voxel of EPI data will therefore
occupy the same space as approximately 64 voxels of SPGR data.
The SPGR data is located under the SPGR_data directory, in 124 I-files
(i.e., I.001 through I.124).
The EPI data is under the EPI_data directory, in a sequence of directories
generated by a GE scanner: 003, 023, 043, 063, 083 and 103. This is
slightly more than 2 runs of data contained in almost 6000 I-files.
Background:
Because of the retinotopic organization of some visual cortical areas,