Your browser doesn't support javascript.
loading
Correcting the bias of empirical frequency parameter estimators in codon models.
Kosakovsky Pond, Sergei; Delport, Wayne; Muse, Spencer V; Scheffler, Konrad.
Afiliación
  • Kosakovsky Pond S; Department of Medicine, University of California San Diego, San Diego, California, United States of America. spond@ucsd.edu
PLoS One ; 5(7): e11230, 2010 Jul 30.
Article en En | MEDLINE | ID: mdl-20689581
Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Codón / Modelos Estadísticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2010 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Codón / Modelos Estadísticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2010 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos