Evidence map›Paper›PMID 25505518›Full record

ArticleEcology and evolution2014

Effective population size does not predict codon usage bias in mammals.

Michael D Kessler, Matthew D Dean

Open access · goldAbstract read
In one paragraph

Article in Ecology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 31 citations in OpenAlex.

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  8. Whole exome sequencing of wild-derived inbred strains of mice improves power to link phenotype and genotype.Mammalian genome : official journal of the International Mammalian Genome Society · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Michael D KesslerMolecular and Computational Biology, University of Southern California 1050 Childs Way, Los Angeles, California, 90089.
Matthew D DeanMolecular and Computational Biology, University of Southern California 1050 Childs Way, Los Angeles, California, 90089.
University of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synonymous codons are not used at equal frequency throughout the genome, a phenomenon termed codon usage bias (CUB). It is often assumed that interspecific variation in the intensity of CUB is related to species differences in effective population sizes (N e), with selection on CUB operating less efficiently in species with small N e. Here, we specifically ask whether variation in N e predicts differences in CUB in mammals and report two main findings. First, across 41 mammalian genomes, CUB was not correlated with two indirect proxies of N e (body mass and generation time), even though there was statistically significant evidence of selection shaping CUB across all species. Interestingly, autosomal genes showed higher codon usage bias compared to X-linked genes, and high-recombination genes showed higher codon usage bias compared to low recombination genes, suggesting intraspecific variation in N e predicts variation in CUB. Second, across six mammalian species with genetic estimates of N e (human, chimpanzee, rabbit, and three mouse species: Mus musculus, M. domesticus, and M. castaneus), N e and CUB were weakly and inconsistently correlated. At least in mammals, interspecific divergence in N e does not strongly predict variation in CUB. One hypothesis is that each species responds to a unique distribution of selection coefficients, confounding any straightforward link between N e and CUB.

Indexed as

Codon usage biaseffective population sizeselection

Identifiers

PMID25505518
PMCPMC4242573
OpenAlexW2094678362

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.