ABCtoolbox: a versatile toolkit for approximate Bayesian computations
Abstract 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 s...
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ftcdlib:qt9275d8h0 2023-05-15T17:12:36+02:00 ABCtoolbox: a versatile toolkit for approximate Bayesian computations Wegmann, Daniel Leuenberger, Christoph Neuenschwander, Samuel Excoffier, Laurent 116 2010-03-04 application/pdf http://www.escholarship.org/uc/item/9275d8h0 english eng eScholarship, University of California http://www.escholarship.org/uc/item/9275d8h0 qt9275d8h0 public Wegmann, Daniel; Leuenberger, Christoph; Neuenschwander, Samuel; & Excoffier, Laurent. (2010). ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics, 11(1), 116. doi: http://dx.doi.org/10.1186/1471-2105-11-116. Retrieved from: http://www.escholarship.org/uc/item/9275d8h0 article 2010 ftcdlib https://doi.org/10.1186/1471-2105-11-116 2016-04-02T18:48:49Z Abstract 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. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results. Article in Journal/Newspaper Microtus arvalis University of California: eScholarship BMC Bioinformatics 11 1 |
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description |
Abstract 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. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results. |
format |
Article in Journal/Newspaper |
author |
Wegmann, Daniel Leuenberger, Christoph Neuenschwander, Samuel Excoffier, Laurent |
spellingShingle |
Wegmann, Daniel Leuenberger, Christoph Neuenschwander, Samuel Excoffier, Laurent ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
author_facet |
Wegmann, Daniel Leuenberger, Christoph Neuenschwander, Samuel Excoffier, Laurent |
author_sort |
Wegmann, Daniel |
title |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_short |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_full |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_fullStr |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_full_unstemmed |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations |
title_sort |
abctoolbox: a versatile toolkit for approximate bayesian computations |
publisher |
eScholarship, University of California |
publishDate |
2010 |
url |
http://www.escholarship.org/uc/item/9275d8h0 |
op_coverage |
116 |
genre |
Microtus arvalis |
genre_facet |
Microtus arvalis |
op_source |
Wegmann, Daniel; Leuenberger, Christoph; Neuenschwander, Samuel; & Excoffier, Laurent. (2010). ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics, 11(1), 116. doi: http://dx.doi.org/10.1186/1471-2105-11-116. Retrieved from: http://www.escholarship.org/uc/item/9275d8h0 |
op_relation |
http://www.escholarship.org/uc/item/9275d8h0 qt9275d8h0 |
op_rights |
public |
op_doi |
https://doi.org/10.1186/1471-2105-11-116 |
container_title |
BMC Bioinformatics |
container_volume |
11 |
container_issue |
1 |
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1766069390338424832 |