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..
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.
- Merge re-sequenced FastQ files (
cat
) - Read QC (
FastQC
) - Adapter and quality trimming (
Trim Galore!
) - Alignment to both target and spike-in genomes (
Bowtie 2
) - Filter on quality, sort and index alignments (
SAMtools
) - Duplicate read marking (
picard MarkDuplicates
) - Create bedGraph files (
BEDTools
- Create bigWig coverage files (
bedGraphToBigWig
) - Peak calling specifically tailored for low background noise (
SEACR
) - Quality control and analysis:
- Genome browser session (
IGV
) - Present QC for raw read, alignment and duplicate reads (
MultiQC
)
-
Install
nextflow
(>=20.04.0
) -
Install any of
Docker
,Singularity
,Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (please only useConda
as a last resort; see docs) -
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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. -
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.
The nf-core/cutandrun pipeline comes with documentation about the pipeline: usage and output.
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.
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).
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.