Notes

This nf-based atac-seq pipeline was originally built by nf-core team. And it was then adapted for axolotl genome. Many features were modified or discarded for the sake of simplicity instead of bing comprehensive.

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. 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 enrichment (deepTools)
   7. Call broad/narrow peaks (MACS2)
   8. Annotate peaks relative to gene features (HOMER)
   9. Create consensus peakset across all samples and create tabular file to aid in the filtering of the data (BEDTools)
   10. Count reads in consensus peaks (featureCounts)
   11. Differential accessibility analysis, PCA and clustering (R, DESeq2)
   12. 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, bedGraphToBigWig)
   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)

Quick Start

1. Install nextflow

2. Install either Docker or Singularity for full pipeline reproducibility (please only use Conda as a last resort; see docs)

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

   nextflow run nf-core/atacseq -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.

4. Start running your own analysis!

   nextflow run nf-core/atacseq -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 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 Bioinformatics & Biostatistics Group for use at The Francis Crick Institute, London.

The pipeline was developed by Harshil Patel.