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Journal Articles BMC Bioinformatics Year : 2019

CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts

Abstract

Background: In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life.Results: We present CoMetGeNe (COnserved METabolic and GEnomic NEighborhoods), a novel method that identifies metabolic and genomic patterns consisting in maximal trails of reactions being catalyzed by products of neighboring genes. Patterns determined by CoMetGeNe in one species are subsequently employed in order to reflect their degree of conservation across multiple prokaryotic species. These interspecies comparisons help to improve genome annotation and can reveal putative alternative metabolic routes as well as unexpected gene ordering occurrences.Conclusions: CoMetGeNe is an exploratory tool at both the genomic and the metabolic levels, leading to insights into the conservation of functionally related clusters of neighboring enzyme-coding genes. The open-source CoMetGeNe pipeline is freely available at https://cometgene.lri.fr

Dates and versions

hal-01979512 , version 1 (13-01-2019)

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Alexandra Zaharia, Bernard Labedan, Christine Froidevaux, Alain Denise. CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts. BMC Bioinformatics, 2019, 20 (1), ⟨10.1186/s12859-018-2542-2⟩. ⟨hal-01979512⟩
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