search

nf-core / cageseq

CAGE-sequencing analysis pipeline with trimming, alignment and counting of CAGE tags.
https://nf-co.re/cageseq
MIT License
11 stars 12 forks source link
cage cage-seq cageseq-data gene-expression nextflow nf-core pipeline rna workflow

readme

nf-core/cageseq

CAGE-seq pipeline.

GitHub Actions CI Status GitHub Actions Linting Status Nextflow DOI

install with bioconda Docker Get help on Slack

Introduction

nf-core/cageseq is a bioinformatics analysis pipeline used for CAGE-seq sequencing data.

The pipeline takes raw demultiplexed fastq-files as input and includes steps for linker and artefact trimming (cutadapt), rRNA removal (SortMeRNA, alignment to a reference genome (STAR or bowtie1) and CAGE tag counting and clustering (paraclu). Additionally, several quality control steps (FastQC, RSeQC, MultiQC) are included to allow for easy verification of the results after a run.

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.

Quick Start

  1. Install nextflow

  2. Install any of Docker, Singularity or Podman 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/cageseq -profile test,<docker/singularity/podman/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/cageseq -profile <docker/singularity/podman/conda/institute> --input '*_R1.fastq.gz' --aligner <'star'/'bowtie1'> --genome GRCh38

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

Pipeline Summary

By default, the pipeline currently performs the following:

  1. Input read QC (FastQC)
  2. Adapter + EcoP15 + 5'G trimming (cutadapt)
  3. (optional) rRNA filtering (SortMeRNA),
  4. Trimmed and filtered read QC (FastQC)
  5. Read alignment to a reference genome (STAR or bowtie1)
  6. CAGE tag counting and clustering (paraclu)
  7. CAGE tag clustering QC (RSeQC)
  8. Present QC and visualisation for raw read, alignment and clustering results (MultiQC)

Documentation

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

Credits

nf-core/cageseq was originally written by Kevin Menden (@KevinMenden) and Tristan Kast (@TrisKast) and updated by Matthias Hörtenhuber (@mashehu).

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

Citations

If you use nf-core/cageseq for your analysis, please cite it using the following doi: 10.5281/zenodo.4095105

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. ReadCube: Full Access Link

In addition, references of tools and data used in this pipeline are as follows:

Nextflow

Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

Pipeline tools

Software packaging/containerisation tools