Cargando…

Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach

Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (K(s)) and non-synonymous (K(a)) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton...

Descripción completa

Detalles Bibliográficos
Autores principales: Stern, Adi, Doron-Faigenboim, Adi, Erez, Elana, Martz, Eric, Bacharach, Eran, Pupko, Tal
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933148/
https://www.ncbi.nlm.nih.gov/pubmed/17586822
http://dx.doi.org/10.1093/nar/gkm382
_version_ 1782134296809570304
author Stern, Adi
Doron-Faigenboim, Adi
Erez, Elana
Martz, Eric
Bacharach, Eran
Pupko, Tal
author_facet Stern, Adi
Doron-Faigenboim, Adi
Erez, Elana
Martz, Eric
Bacharach, Eran
Pupko, Tal
author_sort Stern, Adi
collection PubMed
description Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (K(s)) and non-synonymous (K(a)) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between K(a) and K(s) (ω) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the user's specific needs, including calculation of statistical support of the ω values, an advanced graphic display of the protein's 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the protein's sequence and structure.
format Text
id pubmed-1933148
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-19331482007-07-31 Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach Stern, Adi Doron-Faigenboim, Adi Erez, Elana Martz, Eric Bacharach, Eran Pupko, Tal Nucleic Acids Res Articles Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (K(s)) and non-synonymous (K(a)) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between K(a) and K(s) (ω) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the user's specific needs, including calculation of statistical support of the ω values, an advanced graphic display of the protein's 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the protein's sequence and structure. Oxford University Press 2007-07 2007-06-22 /pmc/articles/PMC1933148/ /pubmed/17586822 http://dx.doi.org/10.1093/nar/gkm382 Text en © 2007 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Stern, Adi
Doron-Faigenboim, Adi
Erez, Elana
Martz, Eric
Bacharach, Eran
Pupko, Tal
Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title_full Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title_fullStr Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title_full_unstemmed Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title_short Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach
title_sort selecton 2007: advanced models for detecting positive and purifying selection using a bayesian inference approach
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933148/
https://www.ncbi.nlm.nih.gov/pubmed/17586822
http://dx.doi.org/10.1093/nar/gkm382
work_keys_str_mv AT sternadi selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach
AT doronfaigenboimadi selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach
AT erezelana selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach
AT martzeric selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach
AT bacharacheran selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach
AT pupkotal selecton2007advancedmodelsfordetectingpositiveandpurifyingselectionusingabayesianinferenceapproach