Exploring the Genomic Landscape of Cancer Patient Cohorts with GenVisR

Zachary L. Skidmore, Katie M. Campbell, Kelsy C. Cotto, Malachi Griffith, Obi L. Griffith

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The creation of visualizations to interpret genomics data remains an important aspect of data science within computational biology. The GenVisR Bioconductor package was created to lower the entry point for publication-quality graphics and has remained a popular suite of tools within this domain. GenVisR supports visualizations covering a breadth of topics including functions to produce visual summaries of copy-number alterations, somatic variants, sequence quality metrics, and more. Recently, the GenVisR package has undergone significant updates to increase performance and functionality. To demonstrate the utility of GenVisR, we present protocols for use of the updated Waterfall() function to create a customizable Oncoprint-style plot of the mutational landscape of a tumor cohort. We explain the basics of installation, data import, configuration, plotting, clinical annotation, and customization. A companion online workshop describing the GenVisR library, Waterfall() function, and other genomic visualization tools is available at genviz.org.

Original languageEnglish
Article numbere252
JournalCurrent Protocols
Volume1
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Bioconductor
  • R
  • genome visualization

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