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Statistical methods for detecting molecular adaptation
The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for d...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science Ltd.
2000
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134603/ https://www.ncbi.nlm.nih.gov/pubmed/11114436 http://dx.doi.org/10.1016/S0169-5347(00)01994-7 |
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author | Yang, Ziheng Bielawski, Joseph P. |
author_facet | Yang, Ziheng Bielawski, Joseph P. |
author_sort | Yang, Ziheng |
collection | PubMed |
description | The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements. |
format | Online Article Text |
id | pubmed-7134603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2000 |
publisher | Elsevier Science Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71346032020-04-08 Statistical methods for detecting molecular adaptation Yang, Ziheng Bielawski, Joseph P. Trends Ecol Evol Article The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements. Elsevier Science Ltd. 2000-12-01 2000-12-06 /pmc/articles/PMC7134603/ /pubmed/11114436 http://dx.doi.org/10.1016/S0169-5347(00)01994-7 Text en Copyright © 2000 Elsevier Science Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Yang, Ziheng Bielawski, Joseph P. Statistical methods for detecting molecular adaptation |
title | Statistical methods for detecting molecular adaptation |
title_full | Statistical methods for detecting molecular adaptation |
title_fullStr | Statistical methods for detecting molecular adaptation |
title_full_unstemmed | Statistical methods for detecting molecular adaptation |
title_short | Statistical methods for detecting molecular adaptation |
title_sort | statistical methods for detecting molecular adaptation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134603/ https://www.ncbi.nlm.nih.gov/pubmed/11114436 http://dx.doi.org/10.1016/S0169-5347(00)01994-7 |
work_keys_str_mv | AT yangziheng statisticalmethodsfordetectingmolecularadaptation AT bielawskijosephp statisticalmethodsfordetectingmolecularadaptation |