nf-core/atacseq

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Introduction

nfcore/atacseq is a bioinformatics analysis pipeline used for ATAC-seq 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 (BWA)
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 mitochondrial DNA (SAMtools)
      • 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 are soft-clipped (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. Create normalised bigWig files scaled to 1 million mapped reads (BEDTools, wigToBigWig)
   4. Call broad/narrow peaks (MACS2)
   5. Annotate peaks relative to gene features (HOMER)
   6. Create consensus peakset across all samples and create tabular file to aid in the filtering of the data (BEDTools)
   7. Count reads in consensus peaks (featureCounts)
   8. Differential accessibility analysis, PCA and clustering (R, DESeq2)
   9. Generate ATAC-seq specific QC html report (ataqv)
6. Merge filtered alignments across replicates (picard)
   1. Re-mark duplicates (picard)
   2. Remove duplicate reads (SAMtools)
   3. Create normalised bigWig files scaled to 1 million mapped reads (BEDTools, wigToBigWig)
   4. Call broad/narrow peaks (MACS2)
   5. Annotate peaks relative to gene features (HOMER)
   6. Create consensus peakset across all samples and create tabular file to aid in the filtering of the data (BEDTools)
   7. Count reads in consensus peaks relative to merged library-level alignments (featureCounts)
   8. Differential accessibility analysis, PCA and clustering (R, DESeq2)
7. Create IGV session file containing bigWig tracks, peaks and differential sites for data visualisation (IGV).
8. Present QC for raw read, alignment, peak-calling and differential accessibility results (ataqv, MultiQC, R)

Documentation

The nf-core/atacseq 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 pipeline was originally written by the The Bioinformatics & Biostatistics Group for use at The Francis Crick Institute, London.

The pipeline was developed by Harshil Patel.

The nf-core/rnaseq and nf-core/chipseq pipelines developed by Phil Ewels were initially used as a template for this pipeline. Many thanks to Phil for all of his help and advice, and the team at SciLifeLab.

Many thanks to others who have helped out along the way too, including (but not limited to): @apeltzer, @sven1103, @MaxUlysse, @micans, @pditommaso.