:warning: UNDER ACTIVE DEVELOPMENT :warning:

Introduction

The nf-core/rnadnavar is a bioinformatics best-practice analysis pipeline for RNA somatic mutation detection able to perform in parallel.

Initially designed for cancer research, the pipeline uses different variant calling algorithms and applies a consensus approach. A final filtering stage, should provide a set of annotated somatic variants.

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

Depending on the options and samples provided, the pipeline will run different tasks. This is controlled mainly through --step and --tools parameters. This is a summary of the possible tasks to run with the pipeline:

• Quality control and trimming (enabled by --trim_fastq and runs FastQC and fastp)
• Map Reads to Reference (BWA-mem, BWA-mem2, dragmap and/or STAR)
• GATK preprocessing for DNA and RNA bulk sequencing samples (GATK MarkDuplicates, GATK SplitNCigarReads,GATK BaseRecalibrator and GATK ApplyBQSR)
• Summarise alignment statistics (samtools stats, mosdepth)
• Variant calling (enabled with --tools)
  • Mutect2
  • Strelka2
  • SAGE
• Annotation with VEP (enabled with --tools adding vep)
• Normalisation of VCFs with VT (enabled with --tools adding normalisation)
• Transformation of VCF to MAF and consensus of variant calling results (enabled with --tools adding consensus)
• Filtering of MAF files applying optional gnomad, whitelisting and blacklisting (enabled with --tools adding filtering)
• Realignment step where reads from regions where a variant was found will be extracted and re-processed, only for RNA due to higher levels of background noise (enabled with --tools adding realignment).
• Filtering of MAF files specific for RNA (enabled with --tools adding rna_filtering)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

Now, you can run the pipeline using:

bash nextflow run nf-core/rnadnavar \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

The nf-core/rnadnavar was originally written by Raquel Manzano Garcia at Cancer Research UK Cambridge Institute with the continuous support of Maxime U Garcia. The workflow is based on RNA-MuTect which was originally published by Yizhak, et al 2019 (Science)

We thank the following people for their assistance in the development of this pipeline: TBC

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 #rnadnavar channel (you can join with this invite).

Citations

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.