Bayes Empirical Bayes Inference of Amino Acid Sites Under Positive Selection

Clicks: 1
ID: 290613
2005
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #181 of 244 articles by views in molecular biology and evolution

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 244 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Codon-based substitution models have been widely used to identify amino acid sites under positive selection in comparative analysis of protein-coding DNA sequences. The nonsynonymous-synonymous substitution rate ratio (dN/dS, denoted ω) is used as a measure of selective pressure at the protein level, with ω > 1 indicating positive selection. Statistical distributions are used to model the variation in ω among sites, allowing a subset of sites to have ω > 1 while the rest of the sequence may be under purifying selection with ω < 1. An empirical Bayes (EB) approach is then used to calculate posterior probabilities that a site comes from the site class with ω > 1. Current implementations, however, use the naive EB (NEB) approach and fail to account for sampling errors in maximum likelihood estimates of model parameters, such as the proportions and ω ratios for the site classes. In small data sets lacking information, this approach may lead to unreliable posterior probability calculations. In this paper, we develop a Bayes empirical Bayes (BEB) approach to the problem, which assigns a prior to the model parameters and integrates over their uncertainties. We compare the new and old methods on real and simulated data sets. The results suggest that in small data sets the new BEB method does not generate false positives as did the old NEB approach, while in large data sets it retains the good power of the NEB approach for inferring positively selected sites.
Reference Key
openalex_W2170565120 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ziheng Yang, Wendy S W Wong, Rasmus Nielsen
Journal molecular biology and evolution
Year 2005
DOI
10.1093/molbev/msi097
URL
https://academic.oup.com/mbe/article-pdf/22/4/1107/3782624/msi097.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.