A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens

Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true v...

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Main Authors: Kayhan Azadmanesh, Sana Eybpoosh
Format: Article
Language:English
Published: Pasteur Institute of Iran 2016-01-01
Series:Journal of Medical Microbiology and Infectious Diseases
Subjects:
Online Access:http://jommid.pasteur.ac.ir/article-1-126-en.html
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author Kayhan Azadmanesh
Sana Eybpoosh
author_facet Kayhan Azadmanesh
Sana Eybpoosh
author_sort Kayhan Azadmanesh
collection DOAJ
description Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at different points in time carry evolutionary information that allow for estimation of evolutionary rates and divergence dates. If the amount of genetic change in the data is proportional to the time elapsed since divergence from the common ancestor, then one can directly estimate the μ from the data. Otherwise, external sources should be used to select the μ value, and use it as a fixed prior in Bayesian evolutionary analysis. This note provides a brief overview on how to assess the adequacy of the evolutionary information in the data and provides some recommendations for obtaining proper evolutionary rate priors from external sources. The recommendations generally highlight the need for the candidate μ prior to be a good representative of the evolutionary rate in the data at hand. This will be achieved by ensuring that the samples that are the source of the candidate μ value have been under relatively similar evolutionary forces as the data at hand. As the evolutionary forces acting on a particular set of samples varies across different study settings and species type, selection of prior for μ should be founded on a thorough understanding of the species under study at biological and social levels.
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spelling doaj.art-306b39ea4b994356886d6165586e1fc02022-12-21T23:45:42ZengPasteur Institute of IranJournal of Medical Microbiology and Infectious Diseases2345-53492345-53302016-01-0141810A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on PathogensKayhan Azadmanesh0Sana Eybpoosh1 Department of Virology, Pasteur Institute of Iran, Tehran, Iran Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran AND HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at different points in time carry evolutionary information that allow for estimation of evolutionary rates and divergence dates. If the amount of genetic change in the data is proportional to the time elapsed since divergence from the common ancestor, then one can directly estimate the μ from the data. Otherwise, external sources should be used to select the μ value, and use it as a fixed prior in Bayesian evolutionary analysis. This note provides a brief overview on how to assess the adequacy of the evolutionary information in the data and provides some recommendations for obtaining proper evolutionary rate priors from external sources. The recommendations generally highlight the need for the candidate μ prior to be a good representative of the evolutionary rate in the data at hand. This will be achieved by ensuring that the samples that are the source of the candidate μ value have been under relatively similar evolutionary forces as the data at hand. As the evolutionary forces acting on a particular set of samples varies across different study settings and species type, selection of prior for μ should be founded on a thorough understanding of the species under study at biological and social levels.http://jommid.pasteur.ac.ir/article-1-126-en.htmlevolutionevolutionary ratebayesian evolutionary analysisphylogeny
spellingShingle Kayhan Azadmanesh
Sana Eybpoosh
A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
Journal of Medical Microbiology and Infectious Diseases
evolution
evolutionary rate
bayesian evolutionary analysis
phylogeny
title A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
title_full A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
title_fullStr A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
title_full_unstemmed A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
title_short A Note on Evolutionary Rate Estimation in Bayesian Evolutionary Analysis: Focus on Pathogens
title_sort note on evolutionary rate estimation in bayesian evolutionary analysis focus on pathogens
topic evolution
evolutionary rate
bayesian evolutionary analysis
phylogeny
url http://jommid.pasteur.ac.ir/article-1-126-en.html
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