#  [](https://github.com/nf-core/rnaseq/actions?query=workflow%3A%22nf-core+CI%22) [](https://github.com/nf-core/rnaseq/actions?query=workflow%3A%22nf-core+linting%22) [](https://nf-co.re/rnaseq/results) [](https://doi.org/10.5281/zenodo.1400710) [](https://www.nextflow.io/) [](https://docs.conda.io/en/latest/) [](https://www.docker.com/) [](https://sylabs.io/docs/) [](https://nfcore.slack.com/channels/rnaseq) [](https://twitter.com/nf_core) [](https://www.youtube.com/c/nf-core) ## Introduction **nf-core/rnaseq** is a bioinformatics analysis pipeline used for RNA sequencing data. The pipeline is built using [Nextflow](https://www.nextflow.io), 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. On release, automated continuous integration tests run the pipeline on a [full-sized dataset](https://github.com/nf-core/test-datasets/tree/rnaseq#full-test-dataset-origin) 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 the full-sized test can be viewed on the [nf-core website](https://nf-co.re/rnaseq/results). ## Pipeline summary 1. Download FastQ files via SRA, ENA or GEO ids and auto-create input samplesheet ([`ENA FTP`](https://ena-docs.readthedocs.io/en/latest/retrieval/file-download.html); *if required*) 2. Merge re-sequenced FastQ files ([`cat`](http://www.linfo.org/cat.html)) 3. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)) 4. UMI extraction ([`UMI-tools`](https://github.com/CGATOxford/UMI-tools)) 5. Adapter and quality trimming ([`Trim Galore!`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)) 6. Removal of ribosomal RNA ([`SortMeRNA`](https://github.com/biocore/sortmerna)) 7. Choice of multiple alignment and quantification routes: 1. [`STAR`](https://github.com/alexdobin/STAR) -> [`featureCounts`](http://bioinf.wehi.edu.au/featureCounts/) 2. [`STAR`](https://github.com/alexdobin/STAR) -> [`RSEM`](https://github.com/deweylab/RSEM) 3. [`HiSAT2`](https://ccb.jhu.edu/software/hisat2/index.shtml) -> [`featureCounts`](http://bioinf.wehi.edu.au/featureCounts/) 8. Sort and index alignments ([`SAMtools`](https://sourceforge.net/projects/samtools/files/samtools/)) 9. UMI-based deduplication ([`UMI-tools`](https://github.com/CGATOxford/UMI-tools)) 10. Duplicate read marking ([`picard MarkDuplicates`](https://broadinstitute.github.io/picard/)) 11. Transcript assembly and quantification ([`StringTie`](https://ccb.jhu.edu/software/stringtie/)) 12. Create bigWig coverage files ([`BEDTools`](https://github.com/arq5x/bedtools2/), [`bedGraphToBigWig`](http://hgdownload.soe.ucsc.edu/admin/exe/)) 13. Extensive quality control: 1. [`RSeQC`](http://rseqc.sourceforge.net/) 2. [`Qualimap`](http://qualimap.bioinfo.cipf.es/) 3. [`dupRadar`](https://bioconductor.org/packages/release/bioc/html/dupRadar.html) 4. [`Preseq`](http://smithlabresearch.org/software/preseq/) 5. [`DESeq2`](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) 14. Pseudo-alignment and quantification ([`Salmon`](https://combine-lab.github.io/salmon/); *optional*) 15. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks ([`MultiQC`](http://multiqc.info/), [`R`](https://www.r-project.org/)) ## Quick Start 1. Install [`nextflow`](https://nf-co.re/usage/installation) 2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`Podman`](https://podman.io/) for full pipeline reproducibility _(please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_. 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: ```bash nextflow run nf-core/rnaseq -profile test, ``` > * Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use `-profile ` in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment. > * If you are using `singularity`, it is highly recommended to use the [`NXF_SINGULARITY_CACHEDIR` or `singularity.cacheDir`](https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub) settings to store the images in a central location for future pipeline runs. 4. Start running your own analysis! * Typical command for RNA-seq analysis: ```bash nextflow run nf-core/rnaseq \ --input samplesheet.csv \ --genome GRCh37 \ -profile ``` * Typical command for downloading public data: ```bash nextflow run nf-core/rnaseq \ --public_data_ids ids.txt \ -profile ``` See [usage docs](https://nf-co.re/rnaseq/usage) for all of the available options when running the pipeline. ## Documentation The nf-core/rnaseq pipeline comes with documentation about the pipeline: [usage](https://nf-co.re/rnaseq/usage) and [output](https://nf-co.re/rnaseq/output). ## Credits These scripts were originally written for use at the [National Genomics Infrastructure](https://ngisweden.scilifelab.se), part of [SciLifeLab](http://www.scilifelab.se/) in Stockholm, Sweden, by Phil Ewels ([@ewels](https://github.com/ewels)) and Rickard Hammarén ([@Hammarn](https://github.com/Hammarn)). The pipeline was re-written in Nextflow DSL2 by Harshil Patel ([@drpatelh](https://github.com/drpatelh)) from [The Bioinformatics & Biostatistics Group](https://www.crick.ac.uk/research/science-technology-platforms/bioinformatics-and-biostatistics/) at [The Francis Crick Institute](https://www.crick.ac.uk/), London. Many thanks to other who have helped out along the way too, including (but not limited to): [@Galithil](https://github.com/Galithil), [@pditommaso](https://github.com/pditommaso), [@orzechoj](https://github.com/orzechoj), [@apeltzer](https://github.com/apeltzer), [@colindaven](https://github.com/colindaven), [@lpantano](https://github.com/lpantano), [@olgabot](https://github.com/olgabot), [@jburos](https://github.com/jburos). ## Contributions and Support If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md). For further information or help, don't hesitate to get in touch on the [Slack `#rnaseq` channel](https://nfcore.slack.com/channels/rnaseq) (you can join with [this invite](https://nf-co.re/join/slack)). ## Citation If you use nf-core/rnaseq for your analysis, please cite it using the following doi: [10.5281/zenodo.1400710](https://doi.org/10.5281/zenodo.1400710) An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](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](https://dx.doi.org/10.1038/s41587-020-0439-x). > ReadCube: [Full Access Link](https://rdcu.be/b1GjZ)