Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.

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Serval ID
serval:BIB_3E96F1A1B541
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.
Journal
Bioinformatics
Author(s)
Tran VDT, Moretti S., Coste A.T., Amorim-Vaz S., Sanglard D., Pagni M.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/07/2019
Peer-reviewed
Oui
Volume
35
Number
13
Pages
2258-2266
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging.
We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.
The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.
Supplementary data are available at Bioinformatics online.
Keywords
Animals, Genome, Metabolic Networks and Pathways, Mice, Probability, Software, Transcriptome
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / CRSII3_141848
Create date
01/07/2019 10:16
Last modification date
26/06/2020 6:21
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