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eclip-peak-1.0.9
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مشاهده بیشترتوضیحات
Pipeline for using IDR to identify a set of reproducible peaks given eClIP dataset with two or three replicates.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | eclip-peak-1.0.9 |
| نام | eclip-peak |
| نسخه کتابخانه | 1.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | FEI YUAN |
| ایمیل نویسنده | fei.yuan@bcm.edu |
| آدرس صفحه اصلی | https://github.com/VanNostrandLab/peak |
| آدرس اینترنتی | https://pypi.org/project/eclip-peak/ |
| مجوز | MIT |
# eCLIP-Peak
Pipeline for using IDR to identify a set of reproducible peaks given eClIP dataset with two or three replicates.
## Installation
- For Van Nostrand Lab
The pipeline has already been installed. Activate its environment
by issue the following command:
`source /storage/vannostrand/software/eclip/venv/environment.sh`.
- For all others:
- Install Python (3.6+)
- Install peak (`pip install eclip-peak`)
- Install [IDR](https://github.com/nboley/idr) (2.0.3+)
- Install Perl (5.10.1+) with the following packages:
- Statistics::Basic (`cpanm install Statistics::Basic`)
- Statistics::Distributions (`cpanm install Statistics::Distributions`)
- install Statistics::R (`cpanm install Statistics::R`)
## Usage
- For Van Nostrand Lab
After activate peak's environment call` peak -h` to see the detailed usage.
- For all others:
After successfully installed Python, peak, Perl (with required packages),
call `peak -h` inside your terminal to see the following detailed usage:
```shell
$ peak -h
usage: peak [-h]
[--ip_bams IP_BAMS [IP_BAMS ...]]
[--input_bams INPUT_BAMS [INPUT_BAMS ...]]
[--peak_beds PEAK_BEDS [PEAK_BEDS ...]]
[--read_type READ_TYPE] [--outdir OUTDIR]
[--species SPECIES]
[--l2fc L2FC] [--l10p L10P] [--idr IDR]
[--dry_run] [--cores] [--debug]
Pipeline for using IDR to identify a set of reproducible peaks given eClIP dataset
with two or three replicates.
optional arguments:
-h, --help show this help message and exit
--ip_bams IP_BAMS [IP_BAMS ...]
Space separated IP bam files (at least 2 files).
--input_bams INPUT_BAMS [INPUT_BAMS ...]
Space separated INPUT bam files (at least 2 files).
--peak_beds PEAK_BEDS [PEAK_BEDS ...]
Space separated peak bed files (at least 2 files).
--ids IDS [IDS ...] Optional space separated short IDs (e.g., S1, S2, S3) for datasets.
--read_type READ_TYPE
Read type of eCLIP experiment, either SE or PE.
--outdir OUTDIR Path to output directory.
--species SPECIES Short code for species, e.g., hg19, mm10.
--l2fc L2FC Only consider peaks at or above this l2fc cutoff, default: 3.
--l10p L10P Only consider peaks at or above this l10p cutoff, default: 3.
--idr IDR Only consider peaks at or above this idr score cutoff, default: 0.01.
--cores CORES Maximum number of CPU cores for parallel processing, default: 1.
--dry_run Print out steps and inputs/outputs of each step without
actually running the pipeline.
--debug Invoke debug mode (only for develop purpose).
```
## Outline of workflow
- Normalize CLIP IP BAM over INPUT for each replicate
- Peak compression/merging on input-normalized peaks for each replicate
- Entropy calculation on IP and INPUT read probabilities within each peak for each replicate
- Run IDR on peaks ranked by entropy
- Normalize IP BAM over INPUT using new IDR peak regions
- Identify reproducible peaks within IDR regions
## Examples
- eCLIP with 2 replicates
Assuming we have eCLIP pipeline run successfully and have the following files generated
for species `hg19`:
```
replicate 1:
IP BAM: ip1.bam
INPUT BAM: input1.bam
Peak BED: clip1.peak.clusters.bed
replicate 2:
IP BAM: ip2.bam
INPUT BAM: input2.bam
Peak BED: clip2.peak.clusters.bed
```
The pipeline then can be called like this to identify reproducible peaks:
```shell
peak \
--ip_bams ip1.bam ip2.bam \
--input_bams input1.bam input2.bam \
--peak_beds clip1.peak.clusters.bed clip2.peak.clusters.bed \
--species hg19
```
- eCLIP with 3 replicates
Assuming we have eCLIP pipeline run successfully and have the following files generated
for species `hg19`:
```
replicate 1:
IP BAM: ip1.bam
INPUT BAM: input1.bam
Peak BED: clip1.peak.clusters.bed
replicate 2:
IP BAM: ip2.bam
INPUT BAM: input2.bam
Peak BED: clip2.peak.clusters.bed
replicate 3:
IP BAM: ip3.bam
INPUT BAM: input3.bam
Peak BED: clip3.peak.clusters.bed
```
The pipeline then can be called like this to identify reproducible peaks:
```shell
peak \
--ip_bams ip1.bam ip2.bam ip3.bam \
--input_bams input1.bam input2.bam input3.bam \
--peak_beds clip1.peak.clusters.bed clip2.peak.clusters.bed clip3.peak.clusters.bed \
--species hg19
```
Note:
- The indentation of the command does not matter, you can write it on the same line.
- The order of bam and peak files followed by `--ip_bams`, `input_bams`, and `peak_beds`
DOES matter, make sure you pass them in a consistent order for these three parameters.
- There are 3 cutoffs can be set for fine tune the peak filtering, see Usage part for
more details.
- If the pipeline failed, check the log to identify the error and make necessary changes,
re-run the pipeline will skip successfully processed parts only continue to processed
failed and unprocessed parts.
## Output
The peak pipeline will output 5 different types of files into the current work directory
or into a user specified output directory (via `--outdir`):
1. *.bed: either a 6 columns or 9 columns bed file saves information for peaks.
2. *.tsv: TSV separated text file saves more information in addition to the BED file.
3. *.txt: text file saves the mapped reads count
4. *.out: TAB separated text file generated by IDR.
5. *.png: plot generated by IDR.
All filenames of output files are self-explained, only the basename of peak bed files (
after the removal of .peak.clusters.bed) was used to mark the name of each replicate.
The reproducible peaks can be found in
*.reproducible.peaks.bed and additional information can be found in *.reproducible.peaks.custom.tsv.
While the former file is 6-column bed file, the later one is a TSV separated text file with the
following columns in order:
- IDR region (entire IDR identified reproducible region)
- Peak (reproducible peak region)
- Geomean of the l2fc
- Columns of log2 fold change (2 or 3 columns for 2 or 3 replicates experiment, respectively)
- Columns of -log10 p-value (2 or 3 columns for 2 or 3 replicates experiment, respectively)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.0 | cmder |
| >=1.2.3 | pandas |
| >=5.3.0 | inflect |
| >=0.0.3 | seqflow |
نحوه نصب
نصب پکیج whl eclip-peak-1.0.9:
pip install eclip-peak-1.0.9.whl
نصب پکیج tar.gz eclip-peak-1.0.9:
pip install eclip-peak-1.0.9.tar.gz