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Commit ecf5c25b authored by Laura Cook's avatar Laura Cook
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### 1. FastQC on raw reads
```
fastqc
```
### 2. Alignment
```
bowtie2-build
```
### 3. Filtering
```
SAMtools sort
SAMtools view
### 4. Alignment QC & Library Complexity
picard MarkDuplicates
```
### 5. deepTools
### 4. Alignment QC & Library Complexity
### 7. phantomPeakQuals
```
SAMtool
### 8. Call narrow peaks (MACS2)
__Effective Genome Length__
picard
We can approximate effective genome size for various read lengths using the khmer program and `unique-kmers.py`. This will estimate the number of unique kmers (for a specified length kmer) which can be used to infer the total uniquely mappable genome. (I.e it doesn't include highly repetitive regions). https://khmer.readthedocs.io/en/v2.1.1/user/scripts.html
preseq
This was a suggestion of deepTools: https://deeptools.readthedocs.io/en/latest/content/feature/effectiveGenomeSize.html
Install khmer program:
```{bash eval=FALSE}
pip3 install khmer
```
### 5. deepTools
Run `unique-kmers.py` on dunnart genome for read length of 150bp:
```
deepTools
```{bash eval=FALSE}
/usr/local/bin/unique-kmers.py -k 150 dunnart_pseudochr_vs_mSarHar1.11_v1.fa
```
### 7. phantomPeakQuals
<details><summary>__Total estimated number of unique 150-mers: 3074798085__</summary>
<p>
```
spp
```
| 150-mers | | | |
|------------------------------------------------------|---------------------|-------------------|-----------------------|
| 3074798085 | | | |
| | | | |
| number of unique k-mers: | 3074798085 | | |
| false positive rate: | 0.010 | | |
| | | | |
| If you have expected false positive rate to achieve: | | | |
| expected_fp | number_hashtable(Z) | size_hashtable(H) | expected_memory_usage |
| 0.100 | 3 | 4.928212e+09 | 1.478464e+10 |
| 0.200 | 2 | 5.187050e+09 | 1.037410e+10 |
| 0.300 | 1 | 8.620729e+09 | 8.620729e+09 |
| 0.400 | 1 | 6.019271e+09 | 6.019271e+09 |
| 0.500 | 1 | 4.435996e+09 | 4.435996e+09 |
| 0.600 | 1 | 3.355701e+09 | 3.355701e+09 |
| 0.700 | 1 | 2.553877e+09 | 2.553877e+09 |
| 0.800 | 1 | 1.910479e+09 | 1.910479e+09 |
| 0.900 | 1 | 1.335368e+09 | 1.335368e+09 |
| | | | |
| If you have expected memory to use: | | | |
| expected_memory_usage | number_hashtable(Z) | size_hashtable(H) | expected_fp |
| 1.000000e+09 | 1 | 1.000000e+09 | 0.954 |
| 5.000000e+09 | 1 | 5.000000e+09 | 0.459 |
| 1.000000e+10 | 2 | 5.000000e+09 | 0.211 |
| 2.000000e+10 | 4 | 5.000000e+09 | 0.045 |
| 5.000000e+10 | 11 | 4.545455e+09 | 0.000 |
| 1.000000e+11 | 22 | 4.545455e+09 | 0.000 |
| 2.000000e+11 | 45 | 4.444444e+09 | 0.000 |
| 3.000000e+11 | 67 | 4.477612e+09 | 0.000 |
| 4.000000e+11 | 90 | 4.444444e+09 | 0.000 |
| 5.000000e+11 | 112 | 4.464286e+09 | 0.000 |
| 1.000000e+12 | 225 | 4.444444e+09 | 0.000 |
| 2.000000e+12 | 450 | 4.444444e+09 | 0.000 |
| 5.000000e+12 | 1127 | 4.436557e+09 | 0.000 |
### 8. Call narrow peaks (MACS2)
```
macs2 callpeaks
```
</p>
</details>
I will use this number as my estimate for effective genome size.
### 9. Create consensus peaksets
### 10. Annotate peaks relative to gene features (HOMER)
### 11. Present QC for raw read, alignment, peak-calling in MultiQC
### 11. Present QC for raw read, alignment, peak-calling in MultiQC
### 12. Plot DAG
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