nf-core/rnaseq
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
3.3
). The latest
stable release is
3.15.0
.
Introduction
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.
On release, automated continuous integration tests run the pipeline on a full-sized dataset obtained from the ENCODE Project Consortium on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from running the full-sized tests individually for each --aligner
option can be viewed on the nf-core website e.g. the results for running the pipeline with --aligner star_salmon
will be in a folder called aligner_star_salmon
and so on.
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
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.
- Merge re-sequenced FastQ files (
cat
) - Read QC (
FastQC
) - UMI extraction (
UMI-tools
) - Adapter and quality trimming (
Trim Galore!
) - Removal of ribosomal RNA (
SortMeRNA
) - Choice of multiple alignment and quantification routes:
- Sort and index alignments (
SAMtools
) - UMI-based deduplication (
UMI-tools
) - Duplicate read marking (
picard MarkDuplicates
) - Transcript assembly and quantification (
StringTie
) - Create bigWig coverage files (
BEDTools
,bedGraphToBigWig
) - Extensive quality control:
- Pseudo-alignment and quantification (
Salmon
; optional) - 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.
Quick Start
-
Install
Nextflow
(>=21.04.0
) -
Install any of
Docker
,Singularity
,Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (please only useConda
as a last resort; see docs). Note: This pipeline does not currently support running with Conda on macOS if the--remove_ribo_rna
parameter is used because the latest version of the SortMeRNA package is not available for this platform. -
Download the pipeline and test it on a minimal dataset with a single command:
nextflow run nf-core/rnaseq -profile test,<docker/singularity/podman/shifter/charliecloud/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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. - If you are using
singularity
then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the--singularity_pull_docker_container
parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use thenf-core download
command to pre-download all of the required containers before running the pipeline and to set theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options to be able to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- 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
-
Start running your own analysis!
nextflow run nf-core/rnaseq \ --input samplesheet.csv \ --genome GRCh37 \ -profile <docker/singularity/podman/conda/institute>
-
An executable Python script called
fastq_dir_to_samplesheet.py
has been provided if you would like to auto-create an input samplesheet based on a directory containing FastQ files before you run the pipeline (requires Python 3 installed locally) e.g.wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py ./fastq_dir_to_samplesheet.py <FASTQ_DIR> samplesheet.csv --strandedness reverse
-
Documentation
The nf-core/rnaseq pipeline comes with documentation about the pipeline usage, parameters and output.
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).
The pipeline was re-written in Nextflow DSL2 by Harshil Patel (@drpatelh) from The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London.
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).
Citations
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 CITATIONS.md
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.