# Research Object Crate for RNA Seq

Original URL: https://dev.workflowhub.eu/workflows/235/ro_crate?version=1

# ![nf-core/rnaseq](docs/images/nf-core-rnaseq_logo.png) [![GitHub Actions CI Status](https://github.com/nf-core/rnaseq/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/rnaseq/actions?query=workflow%3A%22nf-core+CI%22) [![GitHub Actions Linting Status](https://github.com/nf-core/rnaseq/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/rnaseq/actions?query=workflow%3A%22nf-core+linting%22) [![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/rnaseq/results) [![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.1400710-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.1400710) [![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A521.04.0-23aa62.svg?labelColor=000000)](https://www.nextflow.io/) [![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/) [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/) [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/) [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23rnaseq-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/rnaseq) [![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core) [![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core) ## 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](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 running the full-sized tests individually for each `--aligner` option can be viewed on the [nf-core website](https://nf-co.re/rnaseq/results) 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](https://www.nextflow.io), 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](https://www.nextflow.io/docs/latest/dsl2.html) 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](https://github.com/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](https://nf-co.re/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`](http://www.linfo.org/cat.html)) 2. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)) 3. UMI extraction ([`UMI-tools`](https://github.com/CGATOxford/UMI-tools)) 4. Adapter and quality trimming ([`Trim Galore!`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)) 5. Removal of genome contaminants ([`BBSplit`](http://seqanswers.com/forums/showthread.php?t=41288)) 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) -> [`Salmon`](https://combine-lab.github.io/salmon/) 2. [`STAR`](https://github.com/alexdobin/STAR) -> [`RSEM`](https://github.com/deweylab/RSEM) 3. [`HiSAT2`](https://ccb.jhu.edu/software/hisat2/index.shtml) -> **NO QUANTIFICATION** 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/)) > * **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](https://github.com/nf-core/rnaseq/issues/498) for further details. ## Quick Start 1. Install [`Nextflow`](https://www.nextflow.io/docs/latest/getstarted.html#installation) (`>=21.04.0`) 2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/), [`Podman`](https://podman.io/), [`Shifter`](https://nersc.gitlab.io/development/shifter/how-to-use/) or [`Charliecloud`](https://hpc.github.io/charliecloud/) 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 if the `--remove_ribo_rna` parameter is used 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: ```console 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` 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 the [`nf-core download`](https://nf-co.re/tools/#downloading-pipelines-for-offline-use) command to pre-download all of the required containers before running the pipeline and to set the [`NXF_SINGULARITY_CACHEDIR` or `singularity.cacheDir`](https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub) 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 the [`NXF_CONDA_CACHEDIR` or `conda.cacheDir`](https://www.nextflow.io/docs/latest/conda.html) settings to store the environments in a central location for future pipeline runs. 4. Start running your own analysis! ```console nextflow run nf-core/rnaseq \ --input samplesheet.csv \ --genome GRCh37 \ -profile ``` * An executable Python script called [`fastq_dir_to_samplesheet.py`](https://github.com/nf-core/rnaseq/blob/master/bin/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. ```console wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py ./fastq_dir_to_samplesheet.py samplesheet.csv --strandedness reverse ``` ## Documentation The nf-core/rnaseq pipeline comes with documentation about the pipeline [usage](https://nf-co.re/rnaseq/usage), [parameters](https://nf-co.re/rnaseq/parameters) 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 and is primarily maintained by Harshil Patel ([@drpatelh](https://github.com/drpatelh)) from [Seqera Labs, Spain](https://seqera.io/). 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)). ## Citations 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).

Author
  Phil Ewels
License
  MIT

## Contents

Main Workflow: RNA Seq
Size: 2537 bytes