ABCtoolbox: a versatile toolkit for approximate Bayesian computations

By Daniel Wegmann, Christoph Leuenberger, Samuel Neuenschwander, Laurent Excoffier. BMC Bioinformatics, 2010

364 citations recorded by the source corpus.

BackgroundThe 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.ResultsHere 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.ConclusionABCtoolbox 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.

Subfields: Genetics, Paleontology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5b1568e733d2060e4ee13469c97ca3fb764e60a3

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Citations in the corpus

Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows (2010, methodology): “…be directly integrated into an Approximate Bayesian Computation framework, and such an integration has been performed in the ABC-toolbox package (Wegmann et al. , 2010), which allows one to estimate parameters or to perform model choice based on massive simulations (like e.g. in Fagundes et al.…”
Efficient ancestry and mutation simulation with msprime 1.0 (2021, methodology): “…important in simulation-based inference methods such as Approximate Bayesian Computation (ABC) (Beaumont et al. 2002; Csill (cid:2) ery et al. 2010; Wegmann et al. 2010) and machine learning-based approaches (Sheehan and Song 2016; Chan et al. 2018; Schrider and Kern 2018; Flagel et al. 2019;…”
abc: an R package for approximate Bayesian computation (ABC) (2011, background): “…problems have already been developed in these fields (Anderson et al. 2005; Hickerson et al. 2007; Cornuet et al. 2008; Jobin & Mountain 2008; Tallmon et al. 2008; Lopes et al. 2009; Thornton 2009; Bray et al. 2010; Cornuet et al. 2010; Liepe et al. 2010; Wegmann et al. 2010; Huang et al. 2011).”
The complete genome sequence of a Neandertal from the Altai Mountains (2013, neutral): “An approximate Bayesian computation 30 again supports the third scenario (SI 16b) and estimates that 0.5–8% of the Denisovan genome comes from an unknown hominin which split from other hominins 1.1 and 4 million years ago.”
Reconstructing routes of invasion using genetic data: why, how and so what? (2010, background): “The very recent computer package ABCtoolbox (Wegmann et al. 2009) includes a series of open source pro- 2010 Blackwell Publishing Ltd 2010 Blackwell Publishing Ltd sufficient to deal with it (Beaumont et al. 2002).”
Lack of confidence in approximate Bayesian computation model choice (2011, methodology): “• ABCToolbox, which proposes SMC and Markov chain Monte Carlo implementations, as well as Bayes factor approximation (37).”