nfcore/mnaseseq repository overview

⁠Introduction

nfcore/mnaseseq is a bioinformatics analysis pipeline used for DNA sequencing data obtained via micrococcal nuclease digestion.

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 are soft-clipped (BAMTools⁠)
      • reads that have an insert size within specified range (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. Calculate genome-wide coverage assessment (deepTools⁠)
   6. Call nucleosome positions and generate smoothed, normalised coverage bigWig files that can be used to generate occupancy profile plots between samples across features of interest (DANPOS2⁠)
   7. Generate gene-body meta-profile from DANPOS2 smoothed bigWig files (deepTools⁠)
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 nucleosome positions and generate smoothed, normalised coverage bigWig files that can be used to generate occupancy profile plots between samples across features of interest (DANPOS2⁠)
   5. Generate gene-body meta-profile from DANPOS2 smoothed bigWig files (deepTools⁠)
7. Create IGV session file containing bigWig tracks for data visualisation (IGV⁠).
8. Present QC for raw read and alignment results (MultiQC⁠)

⁠Quick Start

i. Install nextflow⁠

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

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

nextflow run nf-core/mnaseseq -profile test,<docker/singularity/conda/institute>

iv. Start running your own analysis!

nextflow run nf-core/mnaseseq -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/mnaseseq 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 along the way too, including (but not limited to): @crickbabs⁠.

⁠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 Slack⁠ (you can join with this invite⁠).

⁠Citation

If you use nf-core/mnaseseq for your analysis, please cite it using the following doi: 10.5281/zenodo.6581372⁠.

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.