jordeu/cutandrun

Analysis pipeline for CUT&RUN and CUT&TAG experiments that includes QC, support for spike-ins, IgG controls, peak calling and downstream analysis.

Analysis pipeline for CUT&RUN and CUT&TAG experiments that includes sequencing QC, spike-in normalisation, IgG control normalisation, peak calling and downstream peak analysis..

Introduction

nf-core/cutandrun is a bioinformatics best-practise analysis pipeline for CUT&Run and CUT&Tag sequencing data analysis to study protein-DNA interactions and epigenomic profiling.

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.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

1. Merge re-sequenced FastQ files (cat)
2. Read QC (FastQC)
3. Adapter and quality trimming (Trim Galore!)
4. Alignment to both target and spike-in genomes (Bowtie 2)
5. Filter on quality, sort and index alignments (SAMtools)
6. Duplicate read marking (picard MarkDuplicates)
7. Create bedGraph files (BEDTools
8. Create bigWig coverage files (bedGraphToBigWig)
9. Peak calling specifically tailored for low background noise (SEACR)
10. Quality control and analysis:
    1. Alignment, fragment length and peak analysis and replicate reproducibility (Python)
    2. Differential peak analysis (DESeq2)
    3. Heatmap peak analysis (deepTools)
11. Genome browser session (IGV)
12. Present QC for raw read, alignment and duplicate reads (MultiQC)

Quick Start

1. Install nextflow (>=20.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud 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/cutandrun -profile test,<docker/singularity/podman/shifter/charliecloud/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!

   • Typical command for CUT&Run/CUT&Tag analysis:

     nextflow run nf-core/cutandrun \
         -profile <docker/singularity/podman/conda/institute> \
         --input samplesheet.csv \
         --genome GRCh37

See usage docs for all of the available options when running the pipeline.

Documentation

The nf-core/cutandrun pipeline comes with documentation about the pipeline: usage and output.

Credits

nf-core/cutandrun was originally written by Chris Cheshire (@chris-cheshire) and Charlotte West (@charlotte-west) from Luscombe Lab at The Francis Crick Institute, London, UK.

The pipeline structure and parts of the downstream analysis were adapted from the original CUT&Tag analysis protocol from the Henikoff Lab.

We thank Harshil Patel (@drpatelh) and everyone in the Luscombe Lab (@luslab) for their extensive assistance in the development of this pipeline.

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

Citations

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:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.