Dataset Information

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

ABSTRACT:

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.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Tran VDT

PROVIDER: S-EPMC6596900 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Tran Van Du T VDT Moretti Sébastien S Coste Alix T AT Amorim-Vaz Sara S Sanglard Dominique D Pagni Marco M

Bioinformatics (Oxford, England) 20190701 13

<h4>Motivation</h4>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.<h4>Results</h4>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 ...[more]

PMID: 30445518

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Project description:BackgroundIdentifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients.ResultsIn this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations.ConclusionsThe analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.

Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

Similar Datasets

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