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ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis.

Johannes Griss1, Guilherme Viteri2, Konstantinos Sidiropoulos2

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom; Department of Dermatology, Medical University of Vienna, Vienna, Austria.

Molecular & Cellular Proteomics : MCP
|September 10, 2020
PubMed
Summary
This summary is machine-generated.

ReactomeGSA simplifies comparative pathway analysis for multi-omics data across species. This tool revealed insights into B cell roles in anti-tumor immunity by analyzing cancer proteomic and transcriptomic data.

Keywords:
Pathway analysisbioinformatics softwarecancer biology*cancer immunologydata evaluationmelanomamulti-omics data integrationtumor microenvironment

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Cancer Research

Background:

  • Pathway analysis is crucial for 'omics data interpretation.
  • Integrating multi-omics and cross-species data presents significant bioinformatics challenges.
  • Existing tools often lack comprehensive support for diverse 'omics datasets and species.

Purpose of the Study:

  • To introduce ReactomeGSA, a novel resource for comparative pathway analysis of multi-omics datasets.
  • To facilitate cross-species pathway analysis by mapping data to a common pathway space.
  • To reduce the technical expertise required for complex 'omics data integration and analysis.

Main Methods:

  • Developed ReactomeGSA, accessible via a web interface and an R Bioconductor package.
  • Implemented automatic mapping of data from different species to a unified pathway framework.
  • Enabled direct integration of public datasets from ExpressionAtlas and Single Cell ExpressionAtlas.
  • Applied ReactomeGSA to analyze transcriptomics (TCGA) and proteomics (CPTAC) data from human cancer samples.

Main Results:

  • ReactomeGSA successfully integrated multi-omics data from different species and sources.
  • Comparative analysis identified that B cell-rich lung adenocarcinoma samples lacked NFkappaB activation.
  • A subset of tumor-associated IgG+ plasma cells in human melanoma showed absent NFkappaB activation in single-cell RNA sequencing (scRNA-seq) data.
  • Demonstrated ReactomeGSA's capability to derive novel biomedical insights from large-scale multi-omics datasets.

Conclusions:

  • ReactomeGSA significantly lowers the barrier for performing sophisticated multi-omics and cross-species pathway analyses.
  • The tool facilitates novel discoveries in cancer immunology, such as the specific role of plasma cells in anti-tumor immunity.
  • ReactomeGSA is a valuable resource for researchers seeking to integrate and interpret complex biological datasets.