nf.core rnaseq

Identifier: WF_86c0ef.c9


nf-core/rnaseq is a bioinformatics pipeline that can be used to analyse RNA sequencing data obtained from organisms with a reference genome and annotation.

Pipeline summary

The SRA download functionality has been removed from the pipeline (>=3.2) and ported to an independent workflow called nf-core/fetchngs. You can provide --nf_core_pipeline rnaseq when running nf-core/fetchngs to download and auto-create a samplesheet containing publicly available samples that can be accepted directly as input by this pipeline.

  1. Merge re-sequenced FastQ files (cat)
  2. Read QC (FastQC)
  3. UMI extraction (UMI-tools)
  4. Adapter and quality trimming (Trim Galore!)
  5. Removal of genome contaminants (BBSplit)
  6. Removal of ribosomal RNA (SortMeRNA)
  7. Choice of multiple alignment and quantification routes:
    1. STAR -> Salmon
    2. STAR -> RSEM
  8. Sort and index alignments (SAMtools)
  9. UMI-based deduplication (UMI-tools)
  10. Duplicate read marking (picard MarkDuplicates)
  11. Transcript assembly and quantification (StringTie)
  12. Create bigWig coverage files (BEDTools, bedGraphToBigWig)
  13. Extensive quality control:
    1. RSeQC
    2. Qualimap
    3. dupRadar
    4. Preseq
    5. DESeq2
  14. Pseudo-alignment and quantification (Salmon; optional)
  15. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (MultiQC, R)
  • NB: Quantification isn't performed if using --aligner hisat2 due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments. However, you can use this route if you have a preference for the alignment, QC and other types of downstream analysis compatible with the output of HISAT2.
  • NB: The --aligner star_rsem option will require STAR indices built from version 2.7.6a or later. However, in order to support legacy usage of genomes hosted on AWS iGenomes the --aligner star_salmon option requires indices built with STAR 2.6.1d or earlier. Please refer to this issue for further details.


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).

The pipeline was re-written in Nextflow DSL2 and is primarily maintained by Harshil Patel (@drpatelh) from Seqera Labs, Spain.

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.

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).


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

An extensive list of references for the tools used by the pipeline can be found in the file.

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.