CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes.
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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.
SUBMITTER: Olo Ndela E
PROVIDER: S-EPMC10356776 | biostudies-literature | 2023 Jul
REPOSITORIES: biostudies-literature
Publications
CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes.
Olo Ndela Eric E Enault François F
Bioinformatics (Oxford, England) 20230701 7
<h4>Summary</h4>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.<h4>Availability a ...[more]
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