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Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
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GOnet: a tool for interactive Gene Ontology analysis.

Mikhail Pomaznoy1, Brendan Ha2, Bjoern Peters2,3

  • 1Department of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA. mikhail@lji.org.

BMC Bioinformatics
|December 12, 2018
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Summary
This summary is machine-generated.

GOnet is an open-source web application that simplifies biological data interpretation by providing interactive visualizations for Gene Ontology (GO) analysis. It bridges the gap between raw data and human understanding of gene/protein functions.

Keywords:
Data analysisGSEAGene ontologyGenomicsInteractiveProteomicsWeb-app

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Interpreting -omics data requires analyzing gene/protein lists.
  • Gene Ontology (GO) enrichment analysis is a common but complex approach.
  • Existing tools lack intuitive, human-interpretable visualization of GO results.

Purpose of the Study:

  • To develop an open-source tool for enhanced biological interpretation of -omics data.
  • To bridge the gap between machine-readable GO analysis output and human understanding.
  • To provide interactive visualization of gene-term and term-term relationships.

Main Methods:

  • Developed the open-source GOnet web application.
  • Implemented GO term annotation and enrichment analysis for human and mouse data.
  • Created interactive graph visualizations of GO analysis results.

Main Results:

  • GOnet generates parsable data formats and interactive visualizations.
  • The application visualizes genes and GO terms as a graph, showing hierarchical relationships.
  • Provides insight into the functional interconnection of submitted gene/protein entries.

Conclusions:

  • GOnet facilitates GO analysis for diverse biological data sources yielding gene/protein lists.
  • The tool aids both experimentalists and computational biologists in interpreting -omics data.
  • Enhances the biological interpretation of complex datasets through interactive visualization.