msABC: A modification of Hudson’s ms to facilitate multi-locus ABC analysis. With the availability of whole-genome sequence data biologists are able to test hypotheses regarding the demography of populations. Furthermore, the advancement of the Approximate Bayesian Computation (ABC) methodology allows the demographic inference to be performed in a simple framework using summary statistics. We present here msABC, a coalescent-based software that facilitates the simulation of multi-locus data, suitable for an ABC analysis. msABC is based on Hudson’s ms algorithm, which is used extensively for simulating neutral demographic histories of populations. The flexibility of the original algorithm has been extended so that sample size may vary among loci, missing data can be incorporated in simulations and calculations, and a multitude of summary statistics for single or multiple populations is generated. The source code of msABC is available at http://bio.lmu.de/ pavlidis/msabc or upon request from the authors.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
- Faisal, Muhammad; Futschik, Andreas; Hussain, Ijaz; Moemen, Mitwali Abd-El.: Choosing summary statistics by least angle regression for approximate Bayesian computation (2016)