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

被引:11
|
作者
Tran, Van Du T. [1 ]
Moretti, Sebastien [1 ,2 ]
Coste, Alix T. [3 ,4 ]
Amorim-Vaz, Sara [3 ,4 ]
Sanglard, Dominique [3 ,4 ]
Pagni, Marco [1 ]
机构
[1] Swiss Inst Bioinformat, Vital IT Grp, CH-1015 Lausanne, Switzerland
[2] Swiss Inst Bioinformat, Evolutionary Bioinformat Grp, CH-1015 Lausanne, Switzerland
[3] Univ Hosp, Inst Microbiol, CH-1015 Lausanne, Switzerland
[4] Univ Lausanne, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
INTEGRATION;
D O I
10.1093/bioinformatics/bty929
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation 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. Results 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. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.
引用
收藏
页码:2258 / 2266
页数:9
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