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.

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 ribosomal RNA (SortMeRNA)
6. Choice of multiple alignment and quantification routes:
   1. STAR -> Salmon
   2. STAR -> RSEM
   3. HiSAT2 -> NO QUANTIFICATION
7. Sort and index alignments (SAMtools)
8. UMI-based deduplication (UMI-tools)
9. Duplicate read marking (picard MarkDuplicates)
10. Transcript assembly and quantification (StringTie)
11. Predict lncRNAs (FEELnc)
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.

Quick Start

1. Install Nextflow (>=21.04.0).

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda 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.

3. 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 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 command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.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 the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. 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.

The pipeline was readapted to be used in the framework of the BovReg project by Jose Espinosa-Carrasco (@joseespinosa) and Björn Langer (@bjlang). The main addition needed by the project was the annotation and quantification of de novo annotated transcripts using StringTie and the prediction of lncRNAs using FEELnc and the output of StringTie. Both features were implemented using as inspiration the TAGADA pipeline from the GENE-SWitCH project. Both the BovReg and the GENE-SWitCH projects form part of the EuroFAANG effort to annotate the genome of farmed animals.

Many thanks to others 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.6973897

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.

Funding

BovReg project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement ID. 815668.

This repository reflects only the listed contributors views. Neither the European Commission nor its Agency REA are responsible for any use that may be made of the information it contains.