COUSIN (COdon Usage Similarity INdex): A Normalized Measure of Codon Usage Preferences.
amino acid composition
bioinformatics
codon adaptation index
codon usage bias
mutational bias
mutation–selection
nucleotide composition
translational selection
Journal
Genome biology and evolution
ISSN: 1759-6653
Titre abrégé: Genome Biol Evol
Pays: England
ID NLM: 101509707
Informations de publication
Date de publication:
01 12 2019
01 12 2019
Historique:
accepted:
25
11
2019
pubmed:
5
12
2019
medline:
29
2
2020
entrez:
5
12
2019
Statut:
ppublish
Résumé
Codon Usage Preferences (CUPrefs) describe the unequal usage of synonymous codons at the gene, chromosome, or genome levels. Numerous indices have been developed to evaluate CUPrefs, either in absolute terms or with respect to a reference. We introduce the normalized index COUSIN (for COdon Usage Similarity INdex), that compares the CUPrefs of a query against those of a reference and normalizes the output over a Null Hypothesis of random codon usage. The added value of COUSIN is to be easily interpreted, both quantitatively and qualitatively. An eponymous software written in Python3 is available for local or online use (http://cousin.ird.fr). This software allows for an easy and complete analysis of CUPrefs via COUSIN, includes seven other indices, and provides additional features such as statistical analyses, clustering, and CUPrefs optimization for gene expression. We illustrate the flexibility of COUSIN and highlight its advantages by analyzing the complete coding sequences of eight divergent genomes. Strikingly, COUSIN captures a bimodal distribution in the CUPrefs of human and chicken genes hitherto unreported with such precision. COUSIN opens new perspectives to uncover CUPrefs specificities in genomes in a practical, informative, and user-friendly way.
Identifiants
pubmed: 31800035
pii: 5652094
doi: 10.1093/gbe/evz262
pmc: PMC6934141
doi:
Types de publication
Letter
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3523-3528Informations de copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
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