The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Variation in the strength of selected codon usage bias among bacteria
Open accessAbstract
Among bacteria, many species have synonymous codon usage patterns that have been influenced by natural selection for those codons that are translated more accurately and/or efficiently. However, in other species selection appears to have been ineffective. Here, we introduce a population genetics-based model for quantifying the extent to which selection has been effective. The approach is applied to 80 phylogenetically diverse bacterial species for which whole genome sequences are available. The strength of selected codon usage bias, S, is found to vary substantially among species; in 30% of the genomes examined, there was no significant evidence that selection had been effective. Values of S are highly positively correlated with both the number of rRNA operons and the number of tRNA genes. These results are consistent with the hypothesis that species exposed to selection for rapid growth have more rRNA operons, more tRNA genes and more strongly selected codon usage bias. For example, Clostridium perfringens, the species with the highest value of S, can have a generation time as short as 7 min.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Thompson, J. D., Higgins, D. G., & Gibson, T. J. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.22.4673 · Cited by 64,617
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
The rapid generation of mutation data matrices from protein sequences
Jones, D. T., Taylor, W. R., & Thornton, J. M. (1992) · Bioinformatics · doi:10.1093/bioinformatics/8.3.275 · Cited by 6,947
Inferring the historical patterns of biological evolution
Pagel, M. (1999) · Nature · doi:10.1038/44766 · Cited by 5,200
Sharp, P. M., & Li, W.-H. (1987) · Nucleic Acids Research · doi:10.1093/nar/15.3.1281 · Cited by 3,992
Self-citationLateral gene transfer and the nature of bacterial innovation
Ochman, H., Lawrence, J. G., & Groisman, E. A. (2000) · Nature · doi:10.1038/35012500 · Cited by 3,816
The ‘effective number of codons’ used in a gene
Wright, F. (1990) · Gene · doi:10.1016/0378-1119(90)90491-9 · Cited by 2,309
Smith, J. M., Smith, N. H., O'Rourke, M., & Spratt, B. G. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.10.4384 · Cited by 1,802
Codon usage and tRNA content in unicellular and multicellular organisms.
(1985) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040335 · Cited by 1,703
Bennetzen, J. L., & Hall, B. D. (1982) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(19)81068-2 · Cited by 1,580
Cited by
Multiple roles of the coding sequence 5′ end in gene expression regulation
Tuller, T., & Zur, H. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1313 · Cited by 206
Tulloch, F., Atkinson, N. J., Evans, D. J., Ryan, M. D., & Simmonds, P. (2014) · eLife · doi:10.7554/elife.04531 · Cited by 178
Genomics of an extreme psychrophile, Psychromonas ingrahamii
Riley, M., Staley, J. T., Danchin, A., Wang, T. Z., Brettin, T. S., Hauser, L. J., Land, M. L., & Thompson, L. S. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-210 · Cited by 126
tRNA properties help shape codon pair preferences in open reading frames
Buchan, J. R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj488 · Cited by 108
Gaunt, E., Wise, H. M., Zhang, H., Lee, L. N., Atkinson, N. J., Nicol, M. Q., Highton, A. J., Klenerman, P., Beard, P. M., Dutia, B. M., et al. (2016) · eLife · doi:10.7554/elife.12735 · Cited by 107
Metadata
- Authors
- Sharp, P. M.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-02-23
- Volume
- 33
- Issue
- 4
- Pages
- 1141-1153
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2143221412
- Cited by
- 429
- References
- 62
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Variation in the strength of selected codon usage bias among bacteria. https://app.cochat.ai/papers/10.1093/nar/gki242
- The paper
- Sharp, P. M. (2005). Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Research. https://doi.org/10.1093/nar/gki242
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.