merge_peaks

CWL-defined pipeline for using IDR to produce a set of peaks given two replicate eCLIP peaks

Installation:

• (For YEOLAB: module load eclipidrmergepeaks/0.1.0)

For all others:

• install CWL
  • pip install cwl
• If Docker is installed, CWL will attempt to pull the following software versions automatically:
  • docker pull brianyee/merge_peaks:0.1.0
  • docker pull brianyee/idr:2.0.2
  • docker pull brianyee/clipper:5d865bb
• (alternatively if Docker is not installed) install packages conventionally:
  • You will need Perl (5.10.1+) with the following packages (Recommended: https://perlbrew.pl):
    • cpan install Statistics::Basic
    • cpan install Statistics::Distributions
    • cpan install Statistics::R
  • In addition, you will need IDR 2.0.2 and Clipper

Outline of workflow:

• Normalize CLIP BAM over INPUT for each replicate (overlap_peakfi_with_bam_PE.cwl)
• Peak compression/merging on input-normalized peaks for each replicate (compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat_outputfull.cwl)
• Entropy calculation on CLIP and INPUT read probabilities within each peak for each replicate (make_informationcontent_from_peaks.cwl)
• Reformat *.full files into *.bed files for each replicate (full_to_bed.cwl)
• Run IDR on peaks ranked by entropy (idr.cwl)
• Calculates summary statistics at different IDR cutoffs (parse_idr_peaks.cwl)
• Normalize CLIP BAM over INPUT using new IDR peak positions (overlap_peakfi_with_bam_PE.cwl)
• Identifies reproducible peaks within IDR regions (get_reproducing_peaks.cwl)

Usage:

(see the examples/merge_peaks_1input.yaml or examples/merge_peaks_2inputs.yaml manifest file for a full example). Below is a description of all fields required to be filled out in the manifest file:

First, use the example template to fill out the names and paths pertaining to your samples. The shebang "#!" line will depend on your experimental setup (either 2 replicates with 2 corresponding inputs, or 2 replicates normalized over 1 input).

This should match what was used to call CLIPper peaks.

species: hg19

BAM file containing the merged-barcode (read 2 only) PCR-deduped CLIP reads mapping to the genome for Replicate 1. Replace "rep1" with a unique ID for each rep1.

    - name: "rep1"
      ip_bam: 
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF994WPX.r2.bam

BAM file containing the merged-barcode (read 2 only) PCR-deduped INPUT reads mapping to the genome for Replicate 1.

      input_bam:
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF590UCY.r2.bam

BED file containing the called peak clusters for Replicate 1 Output from either CLIPPER or input-normed peaks. This pipeline will perform input norm internally for you, so it won't really matter which file you use.

      peak_clusters:
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF639MYI.bed6

BAM file containing the merged-barcode (read 2 only) PCR-deduped CLIP reads mapping to the genome for Replicate 2. Replace "rep2" with a unique ID for each rep2.

    - name: "rep2"
      ip_bam: 
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF154BQS.r2.bam

BAM file containing the merged-barcode (read 2 only) PCR-deduped INPUT reads mapping to the genome for Replicate 2.

      input_bam:
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF590UCY.r2.bam

BED file containing the called peak clusters for Replicate 2 Output from CLIPPER or input-normed peaks. This pipeline will perform input norm internally for you.

      peak_clusters:
        class: File
        path: /home/centos/peCLIP_inputs/ENCFF664WCU.bed6

FINAL OUTPUTS

Merged reproducible peaks are reported as:

rep1.vs.rep2.bed

Where rep1 and rep2 are the user-defined names in the manifest.

To run the workflow:

• Ensure that the yaml file is accessible and that wf_get_reproducible_eclip_peaks.cwl is in your $PATH.
• Type: ./merge_peaks_2inputs.yaml

Outputs

• merged_peaks_bed: this is the BED6 file containing reproducible peaks as determined by entropy-ordered peaks between two replicates.
  • chrom
  • start
  • end
  • minimum of the -log10 p-value between two replicates (coolumn 4)
  • geomean of the log2 fold changes (column 5)
  • strand This is probably what will be useful.
• *.full files: these tabbed outputs have the following columns (in order):
  • chromosome
  • start
  • end
  • name (colon separated region)
  • reads in CLIP
  • reads in INPUT
  • p-value
  • chi value or (F)isher
  • (F)isher or (C)hi square test
  • enriched or depleted
  • negative log10p value (400 if above certain threshold)
  • log2 fold change
  • entropy
• idr.out: output from IDR
• idr.out.bed: output from IDR as a bed file
• *.custombed: contains individual replicate information. The headers are:
  • IDR region (entire IDR identified reproducible region)
  • peak (reproducible peak region)
  • geomean of the l2fc
  • rep1 log2 fold change
  • rep2 log2 fold change
  • rep1 -log10 pvalue
  • rep2 -log10 pvalue

Notes:

• The current conda version of perl installed using create_environment_merge_peaks.sh will install perl v5.22.0, which is different from the version tested on TSCC (5.10.1). Since 5.18, there have been slight changes resulting in hash keys being accessed in a non-deterministic way. Installing 5.22.0 will result in minor changes from the reference, but will otherwise give similar outputs. Included is a script run_perlbrew_perl5.10.1.sh which will attempt to install perl 5.10.1, which will give you deterministic results.
• Custombed files are staged for deprecation, we don't usually use this.