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Expression data analysis with Reactome.

Steve Jupe1, Antonio Fabregat1, Henning Hermjakob1

  • 1European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

Current Protocols in Bioinformatics
|March 11, 2015
PubMed
Summary
This summary is machine-generated.

Reactome pathways visualize gene expression data, aiding in pathway analysis and identifying necessary components. This tool helps track expression changes across experiments, ideal for time-course or disease progression studies.

Keywords:
expression analysismicroarraypathwayquantitative proteomicsreactome

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Molecular Biology

Background:

  • Biological pathways are crucial for understanding cellular functions.
  • Analyzing gene expression data is essential for biological research.
  • Existing tools may lack effective visualization for pathway-specific expression analysis.

Purpose of the Study:

  • To present Reactome as a tool for visualizing user-supplied expression data on pathway diagrams.
  • To enable examination of gene expression within specific biological pathways.
  • To facilitate the assessment of gene presence and expression changes across experimental conditions.

Main Methods:

  • Utilizing the Reactome database for pathway information.
  • Overlaying user-provided expression data onto curated pathway diagrams.
  • Visualizing multiple experiments sequentially to observe expression dynamics.

Main Results:

  • Demonstrated effective visualization of gene expression constituents within pathways.
  • Enabled determination of the presence of necessary pathway components.
  • Facilitated the analysis of expression changes across different experimental conditions.

Conclusions:

  • Reactome offers a valuable tool for interpreting gene expression data in the context of biological pathways.
  • The visualization capabilities aid in understanding pathway dynamics and experimental outcomes.
  • This approach is beneficial for time-course, dose-response, and disease progression studies.