nf-core/clipseq

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Introduction

nf-core/clipseq is a bioinformatics best-practice analysis pipeline for CLIP (cross-linking and immunoprecipitation) sequencing data analysis to study RNA-protein interactions.

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

By default, the pipeline currently performs the following:

1. Adapter and quality trimming (Cutadapt)
2. Pre-mapping to e.g. rRNA and tRNA sequences (Bowtie 2)
3. Genome mapping (STAR)
4. UMI-based deduplication (UMI-tools)
5. Crosslink identification (BEDTools)
6. Bedgraph coverage track generation (BEDTools)
7. Peak calling (multiple options):
   • iCount
   • Paraclu
   • PureCLIP
   • Piranha
8. Motif detection (DREME)
9. Quality control:
   • Sequencing quality control (FastQC)
   • Library complexity (Preseq)
   • Regional distribution (RSeQC)
10. Overall pipeline run and QC summaries and peak calling comparisons (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/clipseq -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!

   nextflow run nf-core/clipseq -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input '[path to design file]' --fasta '[path to genome FASTA]'

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

Documentation

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

Credits

nf-core/clipseq was originally written by Charlotte West (@charlotte-west) and Anob Chakrabarti (@amchakra) from Luscombe Lab at The Francis Crick Institute, London, UK.

It started life in April 2020 as a Nextflow DSL2 Luscombe Lab (@luslab) lockdown hackathon day and we thank all the lab members for their early contributions.

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

Citations

If you use nf-core/clipseq for your analysis, please cite it using the following doi: 10.5281/zenodo.4723016

References of tools and data used in this pipeline can be found in CITATIONS.md

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.