flomicsbiotech/rnaseq Docker Image

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 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 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 [***] 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 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 https://github.com/nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Online videos

A short talk about the history, current status and functionality on offer in this pipeline was given by Harshil Patel (https://github.com/drpatelh) on https://nf-co.re/events/2022/bytesize-32-nf-core-rnaseq as part of the nf-core/bytesize series.

You can find numerous talks on the https://nf-co.re/events 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!

Pipeline summary

The SRA download functionality has been removed from the pipeline (>=3.2) and ported to an independent workflow called 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)
2. Read QC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
3. UMI extraction (https://github.com/CGATOxford/UMI-tools)
4. Adapter and quality trimming (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)
5. Removal of genome contaminants (BBSplit)
6. Removal of ribosomal RNA (https://github.com/biocore/sortmerna)
7. Choice of multiple alignment and quantification routes:
   1. https://github.com/alexdobin/STAR -> https://combine-lab.github.io/salmon/
   2. https://github.com/alexdobin/STAR -> https://github.com/deweylab/RSEM
   3. https://ccb.jhu.edu/software/hisat2/index.shtml -> NO QUANTIFICATION
8. Sort and index alignments (https://sourceforge.net/projects/samtools/files/samtools/)
9. UMI-based deduplication (https://github.com/CGATOxford/UMI-tools)
10. Duplicate read marking (https://broadinstitute.github.io/picard/)
11. Transcript assembly and quantification (https://ccb.jhu.edu/software/stringtie/)
12. Create bigWig coverage files (https://github.com/arq5x/bedtools2/, http://hgdownload.soe.ucsc.edu/admin/exe/)
13. Extensive quality control:
    1. http://rseqc.sourceforge.net/
    2. Qualimap
    3. https://bioconductor.org/packages/release/bioc/html/dupRadar.html
    4. Preseq
    5. https://bioconductor.org/packages/release/bioc/html/DESeq2.html
14. Pseudo-alignment and quantification (https://combine-lab.github.io/salmon/; optional)
15. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (MultiQC, 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.

Quick Start

1. Install https://www.nextflow.io/docs/latest/getstarted.html#installation (>=21.10.3)

2. Install any of https://docs.docker.com/engine/installation/, https://www.sylabs.io/guides/3.0/user-guide/ (you can follow https://singularity-tutorial.github.io/01-installation/), https://podman.io/, https://nersc.gitlab.io/development/shifter/how-to-use/ or https://hpc.github.io/charliecloud/ for full pipeline reproducibility (you can use https://conda.io/miniconda.html both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see 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,YOURPROFILE --outdir <OUTDIR>
   

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check 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 <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, please use the https://nf-co.re/tools/#downloading-pipelines-for-offline-use command to download images first, before running the pipeline. Setting the https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub Nextflow options enables you 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 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 --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>
   

   • An executable Python script called 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 <FASTQ_DIR> samplesheet.csv --strandedness reverse
     

Documentation

The nf-core/rnaseq pipeline comes with documentation about the pipeline https://nf-co.re/rnaseq/usage, https://nf-co.re/rnaseq/parameters and https://nf-co.re/rnaseq/output.

Credits

These scripts were originally written for use at the National Genomics Infrastructure, part of SciLifeLab in Stockholm, Sweden, by Phil Ewels (https://github.com/ewels) and Rickard Hammarén (https://github.com/Hammarn).

The pipeline was re-written in Nextflow DSL2 and is primarily maintained by Harshil Patel (https://github.com/drpatelh) from Seqera Labs, Spain.

The pipeline workflow diagram was designed by Sarah Guinchard (https://github.com/G-Sarah) and James Fellows Yates (https://github.com/jfy133).

Many thanks to other who have helped out along the way too, including (but not limited to): https://github.com/Galithil, https://github.com/pditommaso, https://github.com/orzechoj, https://github.com/apeltzer, https://github.com/colindaven, https://github.com/lpantano, https://github.com/olgabot, https://github.com/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 https://nf-co.re/join/slack).

Citations

If you use nf-core/rnaseq for your analysis, please cite it using the following doi: https://doi.org/10.5281/zenodo.***

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: https://dx.doi.org/10.1038/s41587-020-0439-x.