Skip to content
See the main fork of this repository here >>>
Branch: master
Clone or download
ewels Merge pull request #189 from silviamorins/master
Write out a software_versions.csv file
Latest commit 8646321 Apr 4, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Initial template commit Mar 17, 2019
assets get MultiQC to save plots as stand alone files Mar 29, 2019
bin removed import csv Apr 4, 2019
conf Use defaults for markDuplicates Mar 25, 2019
docs Update version numbers in example code for docs. Fixes #110 [skip-ci] Mar 25, 2019
.gitattributes
.gitignore Merged vanilla TEMPLATE branch into main pipeline Mar 17, 2019
.travis.yml
CHANGELOG.md Update CHANGELOG.md Apr 3, 2019
CODE_OF_CONDUCT.md Initial template commit Mar 17, 2019
Dockerfile
LICENSE Initial template commit Mar 17, 2019
README.md Merged vanilla TEMPLATE branch into main pipeline Mar 17, 2019
environment.yml
main.nf
nextflow.config Bump version to 1.4dev Mar 26, 2019
parameters.settings.json First pass: Manually bringing rnaseq in line with the main nf-core te… Mar 13, 2019

README.md

nf-core/rnaseq

Build Status Nextflow DOI

install with bioconda Docker

Introduction

nf-core/rnaseq is a bioinformatics analysis pipeline used for RNA sequencing data.

The workflow processes raw data from FastQ inputs (FastQC, Trim Galore!), aligns the reads (STAR or HiSAT2), generates gene counts (featureCounts, StringTie) and performs extensive quality-control on the results (RSeQC, dupRadar, Preseq, edgeR, MultiQC). See the output documentation for more details of the results.

The pipeline is built using Nextflow, a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.

Documentation

The nf-core/rnaseq pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation
  2. Pipeline configuration
  3. Running the pipeline
  4. Output and how to interpret the results
  5. Troubleshooting

Credits

These scripts were originally written for use at the National Genomics Infrastructure, part of SciLifeLab in Stockholm, Sweden, by Phil Ewels (@ewels) and Rickard Hammarén (@Hammarn).

Many thanks to other who have helped out along the way too, including (but not limited to): @Galithil, @pditommaso, @orzechoj, @apeltzer, @colindaven.

You can’t perform that action at this time.