nfcore/chipseq repository overview

⁠Introduction

nfcore/chipseq is a bioinformatics analysis pipeline used for Chromatin ImmunopreciPitation sequencing (ChIP-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 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

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/chipseq -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/chipseq -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/chipseq 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

These scripts were originally written by Chuan Wang (@chuan-wang⁠) and Phil Ewels (@ewels⁠) for use at the National Genomics Infrastructure⁠ at SciLifeLab⁠ in Stockholm, Sweden. The pipeline has since been re-implemented by Harshil Patel (@drpatelh⁠) from The Bioinformatics & Biostatistics Group⁠ at The Francis Crick Institute⁠, London.

Many thanks to others who have helped out and contributed along the way too, including (but not limited to): @apeltzer⁠, @bc2zb⁠, @crickbabs⁠, @drejom⁠, @houghtos⁠, @KevinMenden⁠, @mashehu⁠, @pditommaso⁠, @Rotholandus⁠, @sofiahaglund⁠, @tiagochst⁠ and @winni2k⁠.

⁠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 #chipseq channel⁠ (you can join with this invite⁠).

⁠Citation

If you use nf-core/chipseq for your analysis, please cite it using the following doi: 10.5281/zenodo.3240506⁠

You can cite the nf-core publication as follows:

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