nfcore/clipseq repository overview

⁠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>
   

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   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]'
   

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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: