Clinical-Genomics/oncorefiner

Introduction

Clinical-Genomics/oncorefiner is a customizable post-processing and extension layer for nf-core/Oncoanalyser that adapts its outputs according to clinical and operational needs, adds missing analyses, and ensures flexibility for evolving standards while retaining Oncoanalyser's robust core. The pipeline can be split into processes for CNVs, SNVs, SVs, QC and BAM compression.

• CNVs

  1. Generate interactive CNV html report using rmarkdownnotebook, and the script assets/cnv_report.Rmd, based on the result files from oncoanalyser: *.purple.cnv.gene.tsv and *.purple.cnv.somatic.tsv.
  2. Prepare files from Oncoanalysers AMBER and COBALT for visualization using GENS using cutom scripts PREPARE_AMBER_FOR_GENS and PREPARE_COBALT_FOR_GENS which is wrapped in the subworkflow PREPARE_AMBER_COBALT_FOR_GENS.

• SNVs

  1. Annotate the SNV output from Oncoanalyser's PURPLE *.tumor.purple.somatic.vcf.gz using Vcfanno. Intended for local/custom annotation using external databases such as for example GNOMAD, CADD or SweGen (custom configuration settings).
  2. Filter with bcftools. This step aims to apply quality, population-level filtering and/or other general criteria as defined in the configuration settings.
  3. Annotate with CADD. This step annotates indels using CADD resources.
  4. Annotate with Ensembl VEP.
  5. Rank variants and annotate with Genmod score. A score is assigned to each variant based on the genmod score config file provided. Annotation with the score is added to the output vcf file.
  6. Filter with bcftools. This step applies clinically relevant filters as defined in the configuration settings. For example, it may involve subsetting variants based on a a list of clinically relevant genes.

• SVs

  1. Annotate the SV output from Oncoanalyser's PURPLE: *.tumor.purple.sv.vcf.gz with fusions from LINX files using custom scripts in vcf_annotate_linx_fusions.
  2. Annotate VCF with external databases such as for example GNOMAD or SweGen (custom configuration settings) using SVDB.
  3. Filter with bcftools. This step aims to apply quality, population-level filtering and/or other general criteria as defined in the configuration settings.
  4. Annotate with Ensembl VEP.
  5. Rank variants and annotate with Genmod score. A score is assigned to each variant based on the genmod score config file provided. Annotation with the score is added to the output vcf file.
  6. Filter with bcftools. This step applies clinically relevant filters as defined in the configuration settings. For example, it may involve subsetting variants based on a a list of clinically relevant genes.
  7. Generate Cytosure files with vcf2cytosure for visualization in Cytosure.

1. Present QC for raw reads (MultiQC).

• Present QC using MultiQC.

• Compress BAM and BAI files to CRAM and CRAI files using SAMTOOLS_VIEW.

For further information about each step and output files, please refer to the output documentation.

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:

nextflow run Clinical-Genomics/oncorefiner \
   -profile <docker/singularity/.../institute> \
   -params-file <params.yaml/params.json> \
   --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

For more details about the output files and reports, please refer to the output documentation.

Credits

Clinical-Genomics/oncorefiner was originally written by Clinical Genomics Stockholm.

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

• Beatriz Sá Vinhas
• Eva Caceres
• Kristine Bilgrav Sæther
• Felix Lenner
• Mathias Johansson

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

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.