Literature DB >> 31756024

Population Genetic Inference With MIGRATE.

Peter Beerli1, Somayeh Mashayekhi1,2, Marjan Sadeghi1, Marzieh Khodaei1, Kyle Shaw1.   

Abstract

Many evolutionary biologists collect genetic data from natural populations and then need to investigate the relationship among these populations to compare different biogeographic hypotheses. MIGRATE, a useful tool for exploring relationships between populations and comparing hypotheses, has existed since 1998. Throughout the years, it has steadily improved in both the quality of algorithms used and in the efficiency of carrying out those calculations, thus allowing for a larger number of loci to be evaluated. This efficiency has been enhanced, as MIGRATE has been developed to perform many of its calculations concurrently when running on a computer cluster. The program is based on the coalescence theory and uses Bayesian inference to estimate posterior probability densities of all the parameters of a user-specified population model. Complex models, which include migration and colonization parameters, can be specified. These models can be evaluated using marginal likelihoods, thus allowing a user to compare the merits of different hypotheses. The three presented protocols will help novice users to develop sophisticated analysis techniques useful for their research projects.
© 2019 The Authors. Basic Protocol 1: First steps with MIGRATE Basic Protocol 2: Population model specification Basic Protocol 3: Prior distribution specification Basic Protocol 4: Model selection Support Protocol 1: Installing the program MIGRATE Support Protocol 2: Installation of parallel MIGRATE. © 2019 The Authors.

Entities:  

Keywords:  Bayesian inference; DNA; MCMC; coalescent; divergence time; gene flow; microsatellite; population genetics

Mesh:

Year:  2019        PMID: 31756024      PMCID: PMC9286049          DOI: 10.1002/cpbi.87

Source DB:  PubMed          Journal:  Curr Protoc Bioinformatics        ISSN: 1934-3396


  10 in total

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3.  Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach.

Authors:  P Beerli; J Felsenstein
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6.  Bayesian coalescent inference of past population dynamics from molecular sequences.

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7.  Comparison of Bayesian and maximum-likelihood inference of population genetic parameters.

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Journal:  Bioinformatics       Date:  2005-11-29       Impact factor: 6.937

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Journal:  Genetics       Date:  2010-02-22       Impact factor: 4.562

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10.  Population Genetic Inference With MIGRATE.

Authors:  Peter Beerli; Somayeh Mashayekhi; Marjan Sadeghi; Marzieh Khodaei; Kyle Shaw
Journal:  Curr Protoc Bioinformatics       Date:  2019-12
  10 in total
  10 in total

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4.  Population Genetic Inference With MIGRATE.

Authors:  Peter Beerli; Somayeh Mashayekhi; Marjan Sadeghi; Marzieh Khodaei; Kyle Shaw
Journal:  Curr Protoc Bioinformatics       Date:  2019-12

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10.  Biome stability predicts population structure of a southern African aridland bird species.

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  10 in total

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