arhodes77/rnaseq-trinity repository overview

⁠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 counts relative to genes (featureCounts⁠, StringTie⁠) or transcripts (Salmon⁠, tximport⁠ or RSEM⁠) and performs extensive quality-control on the results (RSeQC⁠, Qualimap⁠, dupRadar⁠, Preseq⁠, edgeR⁠, umi_tools⁠, MultiQC⁠).

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 either Docker⁠ or Singularity⁠ for full pipeline reproducibility (please only use Conda⁠ as a last resort; see docs⁠. Note: This pipeline does not currently support running with Conda on macOS because the latest version of the sortmerna package is not available for this platform.)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/rnaseq -profile test,<docker/singularity/conda/institute>
   

   Copy

   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/rnaseq -profile <docker/singularity/conda/institute> --input '*_R{1,2}.fastq.gz' --genome GRCh37
   

   Copy

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

⁠Documentation

The nf-core/rnaseq pipeline comes with documentation about the pipeline which you can read on the nf-core website⁠ or find in the docs/ directory⁠.

⁠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, @lpantano⁠, @olgabot⁠, @jburos⁠, @drpatelh⁠.

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

⁠Citation

If you use nf-core/rnaseq for your analysis, please cite it using the following doi: 10.5281/zenodo.1400710⁠

You can cite the nf-core publication as follows: