iWhale

iWhale detects and annotates somatic variants from cancer Whole Exome Sequencing (WES) data to support comprehensive variant characterization.


Key Features:

  • Docker integration: Encapsulates the pipeline within a Docker container to provide a consistent computational environment.
  • SCons workflow management: Uses SCons to manage workflow dependencies and enable automatic resumption from the last completed step after interruptions.
  • Complementary variant callers: Employs MuTect2, Strelka2, and VarScan2 for somatic variant detection.
  • Unified VCF output: Consolidates outputs from the variant callers into a single Variant Call Format (VCF) file per sample.
  • Variant annotation: Annotates detected variants using information from multiple reference databases for detailed characterization.

Scientific Applications:

  • Somatic variant discovery in cancer WES: Identification and characterization of somatic variants from Whole Exome Sequencing data.
  • Study of cancer genetics: Support for investigations into the genetic underpinnings of cancer using integrated variant calls and annotations.
  • Gene prioritization: Facilitation of variant prioritization for downstream research and analysis.
  • Personalized medicine: Provision of annotated somatic variant data to inform individualized therapeutic considerations.

Methodology:

Uses Docker containerization and SCons for workflow management, runs MuTect2, Strelka2, and VarScan2, consolidates caller outputs into per-sample VCF files, and annotates variants using multiple reference databases.

Topics

Details

Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
11/24/2024

Operations

Publications

Binatti A, Bresolin S, Bortoluzzi S, Coppe A. iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data. Briefings in Bioinformatics. 2020;22(3). doi:10.1093/bib/bbaa065. PMID:32436933. PMCID:PMC8557746.

PMID: 32436933
PMCID: PMC8557746
Funding: - Italian Ministry of Education, Universities and Research: PRIN #2017PPS2X4_003 - Italian Association for Cancer Research: IG #20052, IG #20216, MFAG #15674

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