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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2023

NetSyn: genomic context exploration of protein families

Résumé

Background: The growing availability of large genomic datasets presents an opportunity to discover novel metabolic pathways and enzymatic reactions profitable for industrial or synthetic biological applications. Efforts to identify new enzyme functions in this substantial number of sequences cannot be achieved without the help of bioinformatics tools and the development of new strategies. The classical way to assign a function to a gene uses sequence similarity. However, another way is to mine databases to identify conserved gene clusters (i.e. syntenies) as, in prokaryotic genomes, genes involved in the same pathway are frequently encoded in a single locus with an operonic organisation. This Genomic Context (GC) conservation is considered as a reliable indicator of functional relationships, and thus is a promising approach to improve the gene function prediction. Methods. Here we present NetSyn (Network Synteny), a tool, which aims to cluster protein sequences according to the similarity of their genomic context rather than their sequence similarity. Starting from a set of protein sequences of interest, NetSyn retrieves neighbouring genes from the corresponding genomes as well as their protein sequence. Homologous protein families are then computed to measure synteny conservation between each pair of input sequences using a GC score. A network is then created where nodes represent the input proteins and edges the fact that two proteins share a common GC. The weight of the edges corresponds to the synteny conservation score. The network is then partitioned into clusters of proteins sharing a high degree of synteny conservation. Results. As a proof of concept, we used NetSyn on two different datasets. The first one is made of homologous sequences of an enzyme family (the BKACE family, previously named DUF849)
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Dates et versions

hal-04301713 , version 1 (23-11-2023)

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Paternité - Pas d'utilisation commerciale

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Mark Stam, Jordan Langlois, Céline Chevalier, Guillaume Reboul, Karine Bastard, et al.. NetSyn: genomic context exploration of protein families. 2023. ⟨hal-04301713⟩
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