CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes

Abstract Summary Viral genes, that are frequently small genes and/or with large overlaps, are still difficult to predict accurately. To help predict all genes in viral genomes, we provide CodingDiv that detects SNP-level microdiversity of all potential coding regions, using metagenomic reads and/or...

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Vydáno v:Bioinformatics (Oxford, England) Ročník 39; číslo 7
Hlavní autoři: Olo Ndela, Eric, Enault, François
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford University Press 01.07.2023
Oxford University Press (OUP)
Témata:
Applications Note
Biodiversity and Ecology
Environmental Sciences
ISSN:1367-4811, 1367-4803, 1367-4811
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Shrnutí:Abstract Summary Viral genes, that are frequently small genes and/or with large overlaps, are still difficult to predict accurately. To help predict all genes in viral genomes, we provide CodingDiv that detects SNP-level microdiversity of all potential coding regions, using metagenomic reads and/or similar sequences from external databases. Protein coding regions can then be identified as the ones containing more synonymous SNPs than unfavorable nonsynonymous substitutions SNPs. Availability and implementation CodingDiv is released under the GPL license. Source code is available at https://github.com/ericolo/codingDiv. The software can be installed and used through a docker container.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad408