ArticleBioinformatics (Oxford, England)2019
Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Genes Selectively Expressed in Rat Organs.Current genomics · 2024Article
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology.International journal of molecular sciences · 2023Review
- Age and Behavior-Dependent Differential miRNAs Expression in the Hypopharyngeal Glands of Honeybees (Insects · 2021Article
- CENPL, ISG20L2, LSM4, MRPL3 are four novel hub genes and may serve as diagnostic and prognostic markers in breast cancer.Scientific reports · 2021Article
- MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models.Nucleic acids research · 2021Article
- Anti-adipogenic signals at the onset of obesity-related inflammation in white adipose tissue.Cellular and molecular life sciences : CMLS · 2021Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGenome-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.
resultsWe 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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