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Interactive Browser-Based Genomics Data Visualization Tools for Translational and Clinical Laboratory Applications.

Thomas M Pearce1, Marina N Nikiforova1, Somak Roy1

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This summary is machine-generated.

Three new open-source visualization tools enhance genomics data analysis in clinical settings. These browser-based widgets offer interactive exploration of high-throughput data, improving interpretation and workflow efficiency.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • High-throughput genomics data analysis is crucial for clinical and translational research.
  • Visualization-driven exploration is underutilized in routine clinical workflows.
  • Existing tools lack accessibility and integration for large-scale genomics data.

Purpose of the Study:

  • To develop open-source, browser-based interactive visualization widgets for genomics data.
  • To enhance the interpretation and discovery of insights from complex genomics datasets.
  • To facilitate integration with existing laboratory information systems.

Main Methods:

  • Development of three JavaScript-based widgets using the D3.js library.
  • Widgets designed for client-side data processing to ensure data privacy.
  • jsProteinMapper, jsComut, and jsCodonWheel were created for specific visualization tasks.

Main Results:

  • Developed three accessible, interactive, and customizable genomics visualization widgets.
  • Widgets are browser-based, supporting desktop and mobile devices.
  • Tools facilitate variant interpretation, comutation plot exploration, and codon translation.

Conclusions:

  • These open-source tools can significantly improve laboratory workflows for large-scale genomics data review.
  • The intuitive interface and integration capabilities enhance usability in high-volume settings.
  • The widgets address the need for efficient and accessible genomics data visualization in clinical research.