SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations
Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple mo...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.354.9001 2023-05-15T17:12:35+02:00 SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations Daniel Wegmann Christoph Leuenberger Samuel Neuenschw Laurent Excoffier The Pennsylvania State University CiteSeerX Archives application/zip http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.354.9001 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.354.9001 Metadata may be used without restrictions as long as the oai identifier remains attached to it. ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/3b/18/BMC_Bioinformatics_2010_Mar_4_11_116.tar.gz text ftciteseerx 2016-01-08T00:31:24Z Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. Text Microtus arvalis Unknown |
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Background: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Daniel Wegmann Christoph Leuenberger Samuel Neuenschw Laurent Excoffier |
spellingShingle |
Daniel Wegmann Christoph Leuenberger Samuel Neuenschw Laurent Excoffier SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
author_facet |
Daniel Wegmann Christoph Leuenberger Samuel Neuenschw Laurent Excoffier |
author_sort |
Daniel Wegmann |
title |
SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_short |
SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_full |
SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_fullStr |
SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_full_unstemmed |
SOFTWARE Open Access ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_sort |
software open access abctoolbox: a versatile toolkit for approximate bayesian computations |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.354.9001 |
genre |
Microtus arvalis |
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Microtus arvalis |
op_source |
ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/3b/18/BMC_Bioinformatics_2010_Mar_4_11_116.tar.gz |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.354.9001 |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766069365733588992 |