RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
*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. It takes a samplesheet and FASTQ files as input, performs quality control (QC), trimming and (pseudo-)alignment, and produces a gene expression matrix and extensive QC report.
- Merge re-sequenced FastQ files (
- Sub-sample FastQ files and auto-infer strandedness (
- Read QC (
- UMI extraction (
- Adapter and quality trimming (
- Removal of genome contaminants (
- Removal of ribosomal RNA (
- Choice of multiple alignment and quantification routes:
- Sort and index alignments (
- UMI-based deduplication (
- Duplicate read marking (
- Transcript assembly and quantification (
- Create bigWig coverage files (
- Extensive quality control:
- Pseudo-alignment and quantification (
- Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (
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 rnaseqwhen 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.
Quantification isn't performed if using
--aligner hisat2due 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.
First, you need to prepare a samplesheet with your input data that looks as follows:
sample,fastq_1,fastq_2,strandedness CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,auto CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,auto CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,auto
Each row represents a fastq file (single-end) or a pair of fastq files (paired end). Rows with the same sample identifier are considered technical replicates and merged automatically. The strandedness refers to the library preparation and will be automatically inferred if set to
Please provide pipeline parameters via the CLI or Nextflow
-params-fileoption. Custom config files including those provided by the
-cNextflow option can be used to provide any configuration *except for parameters*; see docs.
Now, you can run the pipeline using:
nextflow run nf-core/rnaseq \ --input samplesheet.csv \ --outdir <OUTDIR> \ --genome GRCh37 \ -profile <docker/singularity/.../institute>
You can find numerous talks on the nf-core events page from various topics including writing pipelines/modules in Nextflow DSL2, using nf-core tooling, running nf-core pipelines as well as more generic content like contributing to Github. Please check them out!
Many thanks to other who have helped out along the way too, including (but not limited to):
- Alex Peltzer
- Colin Davenport
- Denis Moreno
- Edumnd Miller
- Gregor Sturm
- Jacki Buros Novik
- Lorena Pantano
- Matthias Zepper
- Maxime Garcia
- Olga Botvinnik
- Paolo Di Tommaso
- Rob Syme
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
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
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.