Elucidation of Codon Usage Signatures across the Domains of Life
Name
msz124.pdf
Description
Published version
Size
3.12 MB
Format
Adobe PDF
Checksum (MD5)
3f862cd23d351a20c77c1a71a7d598ce
Author(s)
Novoa Pardo, Eva Maria
Jungreis, Irwin
Jaillon, Olivier
Kellis, Manolis
Date Issued
May 2019
Journal
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Citation
Novoa, Eva Maria et al. "Elucidation of Codon Usage Signatures across the Domains of Life." Molecular Biology and Evolution 36, 10 (May 2019): 2328–2339 © 2019 The Author(s)
Version
Final published version
Abstract
Press on behalf of the Society for Molecular Biology and Evolution. Because of the degeneracy of the genetic code, multiple codons are translated into the same amino acid. Despite being "synonymous," these codons are not equally used. Selective pressures are thought to drive the choice among synonymous codons within a genome, while GC content, which is typically attributed to mutational drift, is the major determinant of variation across species. Here, we find that in addition to GC content, interspecies codon usage signatures can also be detected. More specifically, we show that a single amino acid, arginine, is the major contributor to codon usage bias differences across domains of life. We then exploit this finding and show that domain-specific codon bias signatures can be used to classify a given sequence into its corresponding domain of life with high accuracy. We then wondered whether the inclusion of codon usage codon autocorrelation patterns, which reflects the nonrandom distribution of codon occurrences throughout a transcript, might improve the classification performance of our algorithm. However, we find that autocorrelation patterns are not domain-specific, and surprisingly, are unrelated to tRNA reusage, in contrast to previous reports. Instead, our results suggest that codon autocorrelation patterns are a by-product of codon optimality throughout a sequence, where highly expressed genes display autocorrelated "optimal" codons, whereas lowly expressed genes display autocorrelated "nonoptimal" codons.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/molbev/msz124
