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Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing

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nf-core/sarek nf-core/sarek

An open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing

GitHub Actions CI Status GitHub Actions Linting Status AWS CI Cite with Zenodo

Nextflow run with conda run with docker run with singularity Launch on Nextflow Tower

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Introduction

nf-core/sarek is a workflow designed to detect variants on whole genome or targeted sequencing data. Initially designed for Human, and Mouse, it can work on any species with a reference genome. Sarek can also handle tumour / normal pairs and could include additional relapses.

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!

On release, automated continuous integration tests run the pipeline on a full-sized dataset 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 the full-sized test can be viewed on the nf-core website.

It's listed on Elixir - Tools and Data Services Registry and Dockstore.

Pipeline summary

Depending on the options and samples provided, the pipeline can currently perform the following:

  • Form consensus reads from UMI sequences (fgbio)
  • Sequencing quality control and trimming (FastQC, fastp)
  • Map Reads to Reference (BWA-mem or BWA-mem2 or dragmap)
  • Process BAM file (GATK MarkDuplicates, GATK BaseRecalibrator, GATK ApplyBQSR)
  • Summarise alignment statistics (samtools stats, mosdepth)
  • Variant calling (enabled by --tools, see compatibility):
    • HaplotypeCaller
    • freebayes
    • mpileup
    • Strelka2
    • DeepVariant
    • Mutect2
    • Manta
    • TIDDIT
    • ASCAT
    • Control-FREEC
    • CNVkit
    • MSIsensor-pro
  • Variant filtering and annotation (SnpEff, Ensembl VEP)
  • Summarise and represent QC (MultiQC)

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.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

patient,sample,lane,fastq_1,fastq_2
ID1,S1,L002,ID1_S1_L002_R1_001.fastq.gz,ID1_S1_L002_R2_001.fastq.gz

Each row represents a pair of fastq files (paired end).

Now, you can run the pipeline using:

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

See usage docs for all of the available options when running the pipeline.

Documentation

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, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the the results of a 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

Sarek was originally written by Maxime U Garcia and Szilveszter Juhos at the National Genomics Infastructure and National Bioinformatics Infastructure Sweden which are both platforms at SciLifeLab, with the support of The Swedish Childhood Tumor Biobank (Barntumörbanken). Friederike Hanssen and Gisela Gabernet at QBiC later joined and helped with further development.

The Nextflow DSL2 conversion of the pipeline was lead by Friederike Hanssen and Maxime U Garcia.

Maintenance is now lead by Friederike Hanssen and Maxime U Garcia (now at Seqera Labs)

Main developers:

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

Acknowledgements

Barntumörbanken SciLifeLab
National Genomics Infrastructure National Bioinformatics Infrastructure Sweden
QBiC GHGA
DNGC

Contributions & 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 #sarek channel (you can join with this invite), or contact us: Maxime U Garcia, Friederike Hanssen

Citations

If you use nf-core/sarek for your analysis, please cite the Sarek article as follows:

Garcia M, Juhos S, Larsson M et al. Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants [version 2; peer review: 2 approved] F1000Research 2020, 9:63 doi: 10.12688/f1000research.16665.2.

You can cite the sarek zenodo record for a specific version using the following doi: 10.5281/zenodo.3476425

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.

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Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing

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