PePr is a ChIP-Seq Peak-calling and Prioritization pipeline that uses a sliding window approach and models read counts across replicates and between groups with a negative binomial distribution. PePr empirically estimates the optimal shift/fragment size and sliding window width, and estimates dispersion from the local genomic area. Regions with less variability across replicates are ranked more favorably than regions with greater variability. Optional post-processing steps are also made available to filter out peaks not exhibiting the expected shift size and/or to narrow the width of peaks.
- Make sure your python version is higher than 2.6. Version 3.X may not be fully supported yet.
- Install pip in your system if you don't have it.
pip install PeProrpip install PePr --user(if you don't have administrator privilege). Optionally, you can download tarball (PePr-[version].tar.gz) from github and install usingpip install PePr-[version].tar.gz- If installation is successful, you could directly invoke the script by typing
PePr. A help message will show up.
- Single-end: BED, BAM, SAM. For an example of BED format, please see https://genome.ucsc.edu/FAQ/FAQformat.html#format1. BED files need to have at least 6 columns (including chromsome name[1], start[2], end[3], and strand[6]. To estimate shift size (default), you need to have reads from both "+" and "-" strand".)
- Paired-end: BAM, SAM. The files must be sorted by the read names. Users can use
samtools sort -n sample.bam sample.sorted_by_nameto sort the file.
The following scripts are available after the installation and can be called directly from bash console/terminal.
- PePr: the main PePr function. Will run the entire pipline including the parameter estimation and statistical testing. This does not include post-processing step.
- PePr-preprocess: PePr preprocess function. Will only run the parameter estimation step and output a parameter file for the user to use. Users could use/modify the parameter file and then run PePr main function with it.
- PePr-postprocess: PePr postprocess function. This function will take PePr peaks and remove suspected false positives using the peak shapes. The ChIP and input files of the group where the peaks from are required for this function. For example, chip1 and input1 files will be needed for chip1_peaks. This function loads all of the reads into memory, so if you don't have enough memory on your machine, you don't have to give PePr all ChIP/input files. 2 to 3 files might suffice under certain circumstances.
Warning: These are working examples with minimal required parameters. For the best performance (or to avoid bad fitting) on your data, please read this manual carefully and choose the right parameters.
- For peak-calling, run:
PePr -c chip_rep1.bam,chip_rep2.bam -i input_rep1.bam,input_rep2.bam -f bam
- For differential binding analysis with input samples, run:
PePr -c chip1_rep1.bam,chip1_rep2.bam -i input1_rep1.bam,input1_rep2.bam --chip2 chip2_rep1.bam,chip2_rep2.bam --input2 input2_rep2.bam,input2_rep2.bam -f bam --diff
- For differential binding analysis without input samples, run:
PePr -c chip1_rep1.bam,chip1_rep2.bam --chip2 chip2_rep1.bam,chip2_rep2.bam -f bam --diff
- To use a parameter file, run:
PePr -p parameter_file.txt. For how to write a parameter file, see the sectionParameter Filebelow.
| Parameter | Description |
|---|---|
| -p/--parameter-file | Use parameter file instead of command line options. Using a parameter file will ignore all other command line options. See the next section for parameter file configuration. |
| -i/--input1 | Group 1 input files. Multiple file names are separated by comma, e.g. input1.bam,input2.bam. you can also specify relative path to the file names, like folder1/input1.bam,folder2/input2.bam,folder3/input3.bam |
| -c/--chip1 | Group 1 ChIP files. |
| --input2 | Group 2 input files. Use in differential binding analysis. |
| --chip2 | Group 2 ChIP files. Use in differential binding analysis. |
| -n/--name | Experiment name. It will be prefix to all output files from PePr. Default: "NA" |
| -f/--file-format | Read file format. Currently support bed, sam, bam, sampe (sam paired-end), bampe (bam paired-end) |
| -s/--shiftsize | Half the fragment size. The number of bases to shift forward and reverse strand reads toward each other. If not specified by user, PePr will empirically estimate this number from the data for each ChIP sample. |
| -w/--windowsize | Sliding window size. If not specified by user, PePr will estimate this by calculating the average width of potential peaks. The lower and upper bound for PePr estimate is 100bp and 1000bp. User provided window size is not constrained, but we recommend to stay in this range (100-1000bp). |
| --diff | Tell PePr to perform differential binding analysis. |
| --threshold | p-value cutoff. Default:1e-5. |
| --peaktype | sharp or broad. Default is broad. PePr treats broad peaks (like H3k27me3) and sharp peaks(like most transcriptions factors) slightly different. Specify this option if you know the feature of the peaks. |
| --normalization | inter-group, intra-group, scale, or no. Default is intra-group for peak-calling and inter-group for differential binding analysis. PePr is using a modified TMM method to normalize for the difference in IP efficiencies between samples (see the supplementary methods of the paper). It is making an implicit assumption that there is substantial overlap of peaks in every sample. However, it is sometimes not true between groups (for example, between TF ChIP-seq and TF knockout). So for differential binding analysis, switch to intra-group normalization. scale is simply scaling the reads so the total library sizes are the same. no normalization will not do normalization. |
| --keep-max-dup | maximum number of duplicated reads at each single position to keep. If not specified, will not remove any duplicate. |
| --num-processors | Number of CPUs to run in parallel. |
| --input-directory | where the data files are. The path specified here will be a prefix added to each of the files. The best practice is to always use absolute path in here. |
| --output-directory | where you want the output files to be. PePr will add this path as a prefix to the output files. It is recommended to use the absolute path. |
| --version | Will show the version number and exit. |
The parameter file is an easier way of running PePr by including the running parameters in one file. It is effectively the same as running from the command line. A basic example is provided below:
#filetype filename
chip1 chip_rep1.bed
chip1 chip_rep2.bed
input1 input_rep1.bed
input1 input_rep2.bed
file-format bed
peaktype broad
difftest FALSE
keep-max-dup 2
threshold 1e-5
name test
PePr will also output a complete parameter file for you to keep a record of your running parameters and produce the same results.
- NAME__PePr_peaks.bed: A tab-delimited file containing chromosomal position of the peak, name, signal value, fold change, p-value and Benjamini-Hochberg FDR. Peak format is same as the ENCODE BroadPeak format.
- NAME__PePr_[chip1/2]_peaks.bed: this is the same as above, but only available when you run in differential binding mode. "chip1_peaks" are enriched in chip1, "chip2_peaks* are enriched in chip2.
- NAME__PePr_parameters.txt: A file containing the parameters to reproduce the results.
- NAME-Date-Time-SessionID-debug.log: This file contains the detailed information about the running status. Useful debugging information contains: Chromosomes analyzed, shift size and window size estimation, number of candidate windows, etc.
- https://github.com/shawnzhangyx/PePr/ # Source code
- https://renlab.sdsc.edu/omics/tags/PePr/ # PePr FAQ
- https://pypi.python.org/pypi/pepr #PyPI package index
You're also welcome to shoot me an e-mail at [email protected], I'll try replying to you as soon as possible. In the e-mail, please include [1] a copy of your command/script to call PePr, [2] paramters.txt file, and [3] log file. It will speed up the troubleshooting process.
Zhang Y, Lin YH, Johnson TD, Rozek LS, Sartor MA. PePr: A peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data. Bioinformatics. 2014.