nf-core/cutandrun
Analysis pipeline for CUT&RUN and CUT&TAG experiments that includes QC, support for spike-ins, IgG controls, peak calling and downstream analysis.
3.0
). The latest
stable release is
3.2.2
.
Introduction
nf-core/cutandrun is a best-practice bioinformatic analysis pipeline for CUT&RUN and CUT&Tag experimental protocols that were developed to study protein-DNA interactions and epigenomic profiling.
Meers, M. P., Bryson, T. D., Henikoff, J. G., & Henikoff, S. (2019). Improved CUT&RUN chromatin profiling tools. eLife, 8. https://doi.org/10.7554/eLife.46314
Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1), 1930. https://doi.org/10.1038/s41467-019-09982-5]
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a portable, reproducible manner. It is capable of using containerisation and package management making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process, which makes it easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules.
The pipeline has been developed with continuous integration (CI) and test driven development (TDD) at its core. nf-core code and module linting as well as a battery of over 100 unit and integration tests run on pull request to the main repository and on release of the pipeline. On official release, automated CI tests run the pipeline on a full-sized dataset on 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
- Check input files
- 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
) - Create bedGraph files (
bedtools
- Create bigWig coverage files (
bedGraphToBigWig
) - Peak calling (
SEACR
,MACS2
) - Consensus peak merging and reporting (
bedtools
) - Library complexity ([preseq](Preseq | The Smith Lab))
- Fragment-based quality control (
deepTools
) - Peak-based quality control (
bedtools
, custom python) - Heatmap peak analysis (
deepTools
) - Genome browser session (
IGV
) - Present all QC in web-based report (
MultiQC
)
Quick Start
-
Install
Nextflow
(>=21.10.3
) -
Install any of
Docker
,Singularity
(you can follow this tutorial),Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (you can useConda
both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs). -
Download the pipeline and test it on a minimal dataset with a single command:
Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (
YOURPROFILE
in the example command above). You can chain multiple config profiles in a comma-separated string.- The pipeline comes with config profiles called
docker
,singularity
,podman
,shifter
,charliecloud
andconda
which instruct the pipeline to use the named tool for software management. For example,-profile test,docker
. - 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. - If you are using
singularity
, please use thenf-core download
command to download images first, before running the pipeline. Setting theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options enables you to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- The pipeline comes with config profiles called
docker
,singularity
,podman
,shifter
,charliecloud
andconda
which instruct the pipeline to use the named tool for software management. For example,-profile test,docker
. - 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. - If you are using
singularity
, please use thenf-core download
command to download images first, before running the pipeline. Setting theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options enables you to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- The pipeline comes with config profiles called
-
Start running your own analysis!
- Typical command for CUT&Run/CUT&Tag analysis:
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, parameters 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
If you use nf-core/cutandrun for your analysis, please cite it using the following doi: 10.5281/zenodo.5653535
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.