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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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vcfView: An Extensible Data Visualization and Quality Assurance Platform for Integrated Somatic Variant Analysis.

Brian O'Sullivan1, Cathal Seoighe1

  • 1School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.

Cancer Informatics
|November 26, 2020
PubMed
Summary
This summary is machine-generated.

vcfView is an interactive R tool that helps researchers evaluate somatic mutation calls from cancer sequencing data. It allows exploration of variant calls, improving sensitivity and guiding future experiments.

Keywords:
COSMIC v3RSBSSNVVCFcancerindelshinyvariant filteringvisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic mutations are crucial for cancer prognosis and treatment.
  • High-throughput sequencing is vital for discovering novel cancer driver mutations.
  • Distinguishing somatic mutations from germline variants and errors is a significant bioinformatics challenge.

Purpose of the Study:

  • To present vcfView, an interactive tool for evaluating somatic mutation calls in cancer sequencing data.
  • To enable researchers to explore the impact of analytical choices on mutation spectrum and gene variants.
  • To facilitate re-examination of variants and guide future experimental design.

Main Methods:

  • Developed vcfView as an interactive R-based Shiny application.
  • Input is a single variant call format (VCF) file.
  • Allows exploration of mutant allele frequency spectrum, mutational signatures, and annotated somatic variants.

Main Results:

  • vcfView supports evaluation of somatic mutation calls.
  • Researchers can explore the impact of analytical choices on variant data.
  • The tool aids in improving sensitivity and designing future experiments.

Conclusions:

  • vcfView is an extensible tool for analyzing somatic mutations from high-throughput sequencing data.
  • It aids researchers in refining variant calls and optimizing experimental strategies.
  • The application is platform-independent and available on GitHub.