nf-core/rnadnavar

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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

Key requirements for the analysis

• RNA tumor samples must be associated with a DNA normal sample using the same patient ID. DNA tumour sample is optional.
• Sample types are specified with status: 0 (normal DNA), 1 (tumor DNA), 2 (tumor RNA)
• Reference files are required for the analysis.

Quick Start

The typical command for running the pipeline is:

nextflow run nf-core/rnadnavar \
   -r <VERSION> \
   -profile <PROFILE> \
   --input samplesheet.csv \
   --outdir <OUTDIR> \
   --genome <GENOME> \
   --tools <TOOLS>

Pipeline Steps

The pipeline can start from different entry points but tools need to be specified to run. If you are planning to run the pipeline by stages please read usage.

Input requirements

You will need to create a samplesheet with information about the samples you want to analyze. The samplesheet must be a comma-separated file with a header row.

Essential columns are:

• patient - Unique patient identifier
• status - Sample type (0/1/2)
• sample - Unique sample identifier
• And one of these options:
  • if variant calling is going to be performed of these three options should be an input (note that this is included in the realignment step by default):
    • fastq_1, fastq_2 - Paths to paired-end FASTQ files (for raw data) (this also needs lane column)
    • bam,bai - Paths to BAM files and indices (for pre-aligned data)
    • cram,crai - Paths to CRAM files and indices (for pre-aligned data)
  • if the starting step is from consensus and no realignment will be performed (add --skip_tools 'realignment' to your command or params file) then the input are VCF/MAF:
    • caller - Caller used to generate that MAF (to annotate it during the consensus approach)
    • maf - Paths to MAF file
  • if realignment is desired then please add normal_id and take a look to usage.

Examples CSVs:

Starting from mapping:

patient,sample,status,lane,fastq_1,fastq_2
PT1,DNA_NORMAL_SAMPLE,0,LX,DNA_NORMAL_SAMPLE_L002_R1_001.fastq.gz,DNA_NORMAL_SAMPLE1_L002_R2_001.fastq.gz
PT1,DNA_TUMOUR_SAMPLE,1,LX,DNA_TUMOUR_SAMPLE_L002_R1_001.fastq.gz,DNA_TUMOUR_SAMPLE_L002_R2_001.fastq.gz
PT1,RNA_TUMOUR_SAMPLE,2,LX,RNA_TUMOUR_SAMPLE_L002_R1_001.fastq.gz,RNA_TUMOUR_SAMPLE_L002_R2_001.fastq.gz

Starting from consensus no realignment:

patient,sample,status,caller,maf
PT1,RNA_TUMOUR_SAMPLE_1,2,mutect,DNA_TUMOUR_SAMPLE_1.mutect.maf
PT1,RNA_TUMOUR_SAMPLE_1,2,strelka,RNA_TUMOUR_SAMPLE_1.strelka.maf
PT1,RNA_TUMOUR_SAMPLE_2,2,mutect,DNA_TUMOUR_SAMPLE_2.mutect.maf
PT1,RNA_TUMOUR_SAMPLE_2,2,strelka,RNA_TUMOUR_SAMPLE_2.strelka.maf

Parameters

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.