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Updated: Jun 21, 2026
07:28
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
An integrative network approach to map the transcriptome to the phenome
Michael R Mehan1, Juan Nunez-Iglesias, Mrinal Kalakrishnan
1Program in Computational Biology, Department of Biological Sciences, University of Southern California , Los Angeles, CA 90089, USA.
Summary
This study maps gene coexpression modules to phenotypes genome-wide. Researchers identified 118,772 modules linked to 42 phenotypes, creating robust gene network-phenotype maps.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Identifying cooperating gene groups is established, but linking them to specific phenotypes remains challenging.
- Previous research has not established genome-wide maps of gene coexpression modules to the phenome.
Purpose of the Study:
- To present the first genome-wide mapping of gene coexpression modules onto the phenome.
- To develop a robust method for identifying phenotype-specific gene coexpression modules.
Main Methods:
- Annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS).
- Employed a graph-based simulated annealing approach to identify recurrent and specific coexpression modules for individual phenotypes.
Main Results:
- Discovered 118,772 gene coexpression modules specific to 42 distinct phenotypes.
- Validated findings using Gene Ontology, GeneRIF, and UMLS, ensuring robustness through phenotype-specific recurrence.
Conclusions:
- The developed method provides a robust approach for genome-wide gene network-phenotype mapping.
- This methodology is broadly applicable to network data with defined phenotype associations, advancing biological network research.
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