nfcore/atacseq repository overview

⁠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>
   

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   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
   

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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.

Many thanks to others who have helped out and contributed along the way too, including (but not limited to): @ewels⁠, @apeltzer⁠, @crickbabs⁠, drewjbeh⁠, @houghtos⁠, @jinmingda⁠, @ktrns⁠, @MaxUlysse⁠, @mashehu⁠, @micans⁠, @pditommaso⁠ and @sven1103⁠.

⁠Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines⁠.

For further information or help, don't hesitate to get in touch on the Slack #atacseq channel⁠ (you can join with this invite⁠).

⁠Citation

If you use nf-core/atacseq for your analysis, please cite it using the following doi: 10.5281/zenodo.2634132⁠

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md⁠ file.

You can cite the nf-core publication as follows: