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SubcellulaRVis: a web-based tool to simplify and visualise subcellular compartment enrichment.

Joanne Watson1,2, Michael Smith2, Chiara Francavilla2,3

  • 1Division of Evolution, Infection & Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK.

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Summary

SubcellulaRVis is a new bioinformatics tool that visualizes Gene Ontology Cellular Component (GOCC) enrichment results. This tool helps biologists quickly interpret protein subcellular localization from high-throughput omics data.

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Area of Science:

  • Cellular Biology
  • Bioinformatics
  • Genomics

Background:

  • Protein localization within cellular compartments is critical for function and regulation.
  • High-throughput omics studies generate large gene/protein lists, requiring functional interpretation.
  • Understanding subcellular localization aids in deciphering protein roles and cellular processes.

Purpose of the Study:

  • To develop a bioinformatics tool, SubcellulaRVis, for visualizing Gene Ontology Cellular Component (GOCC) enrichment results.
  • To enable quick and intuitive interpretation of subcellular localization for groups of proteins.
  • To assist experimental biologists in analyzing protein (re)localization data.

Main Methods:

  • Designed and implemented the SubcellulaRVis bioinformatics tool.
  • Utilized statistical enrichment analysis for Gene Ontology Cellular Component (GOCC) annotations.
  • Validated the tool's precision using gene lists with previously ascertained subcellular localizations.

Main Results:

  • SubcellulaRVis provides compelling visualizations of GOCC enrichment.
  • The tool accurately describes the subcellular localization of gene lists.
  • Demonstrated precise localization interpretation for groups of proteins.

Conclusions:

  • SubcellulaRVis offers an effective solution for interpreting protein subcellular localization from omics data.
  • The tool is accessible via the web and as a standalone application.
  • SubcellulaRVis empowers biologists with limited bioinformatics expertise to analyze localization data.