NOTICE

This is forked from markgene/chipseq, which itself is forked from nf-core/chipseq configed for running on SJ HPC. This repo focuses on CUT&RUN instead of regular ChIP-seq.

Introduction

markgene/cutnrun is a bioinformatics analysis pipeline used for CUT&RUN data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Pipeline summary

1. Raw read QC (FastQC)
2. Adapter trimming (Trim Galore!)
3. Alignment (Bowtie2)
4. Mark duplicates (picard)
5. Merge alignments from multiple libraries of the same sample (picard)
   1. Re-mark duplicates (picard)
   2. Filtering to remove:
      • reads mapping to blacklisted regions (SAMtools, BEDTools)
      • reads that are marked as duplicates (SAMtools)
      • reads that arent marked as primary alignments (SAMtools)
      • reads that are unmapped (SAMtools)
      • reads that map to multiple locations (SAMtools)
      • reads containing > 4 mismatches (BAMTools)
      • reads that have an insert size > 2kb (BAMTools; paired-end only)
      • reads that map to different chromosomes (Pysam; paired-end only)
      • reads that arent in FR orientation (Pysam; paired-end only)
      • reads where only one read of the pair fails the above criteria (Pysam; paired-end only)
   3. Alignment-level QC and estimation of library complexity (picard, Preseq)
   4. Create normalised bigWig files scaled to 1 million mapped reads (BEDTools, bedGraphToBigWig)
   5. Generate gene-body meta-profile from bigWig files (deepTools)
   6. Calculate genome-wide IP enrichment relative to control (deepTools)
   7. Calculate strand cross-correlation peak and ChIP-seq quality measures including NSC and RSC (phantompeakqualtools)
   8. Call broad/narrow peaks (MACS2)
   9. Annotate peaks relative to gene features (HOMER)
   10. Create consensus peakset across all samples and create tabular file to aid in the filtering of the data (BEDTools)
   11. Count reads in consensus peaks (featureCounts)
   12. Differential binding analysis, PCA and clustering (R, DESeq2)
6. Create IGV session file containing bigWig tracks, peaks and differential sites for data visualisation (IGV).
7. Present QC for raw read, alignment, peak-calling and differential binding results (MultiQC, R)

Quick Start

i. Install nextflow

ii. Install one of docker, singularity or conda

iii. Download the pipeline and test it on a minimal dataset with a single command

nextflow run markgene/cutnrun -profile test,<docker/singularity/conda/institute>

Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile institute in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.

iv. Start running your own analysis!

nextflow run nf-core/chipseq -profile <docker/singularity/conda/institute> --input design.csv --genome GRCh37

See usage docs for all of the available options when running the pipeline.

Documentation

The markgene/cutnrun pipeline comes with documentation about the pipeline, found in the docs/ directory:

1. Installation
2. Pipeline configuration
   • Local installation
   • Adding your own system config
   • Reference genomes
3. Running the pipeline
4. Output and how to interpret the results
5. Troubleshooting

Credits

The workflow was originally forked from nf-core/chipseq. I modify the codes to make it fit better for CUT&RUN data.