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msABC: a modification of Hudson's ms to facilitate multi-locus ABC analysis
P Pavlidis1, S Laurent, W Stephan
1Department of Biology II, Section of Evolutionary Biology, University of Munich, Grosshaderner Strasse 2, 82152 Planegg-Martinsried, Germany.
Molecular Ecology Resources
|May 14, 2011
Summary
This study introduces msABC, a new software tool for simulating population genetics data. It aids in testing demographic hypotheses using whole-genome data and Approximate Bayesian Computation (ABC).
Area of Science:
- Population genetics
- Computational biology
- Bioinformatics
Background:
- Whole-genome sequence data enables testing of population demography hypotheses.
- Approximate Bayesian Computation (ABC) provides a framework for demographic inference using summary statistics.
Purpose of the Study:
- To present msABC, a coalescent-based software for simulating multi-locus data.
- To facilitate Approximate Bayesian Computation (ABC) analyses in population genetics.
Main Methods:
- msABC is based on Hudson's ms algorithm for simulating neutral demographic histories.
- The software extends the original algorithm to handle variable sample sizes across loci and incorporate missing data.
- It generates numerous summary statistics for single or multiple populations.
Main Results:
- msABC facilitates the simulation of multi-locus genetic data.
- The software is suitable for Approximate Bayesian Computation (ABC) analyses.
- It offers enhanced flexibility compared to the original ms algorithm.
Conclusions:
- msABC is a valuable tool for researchers studying population demography using genomic data.
- The software simplifies the process of generating data for ABC analyses.
- It supports advanced simulation scenarios, including varying sample sizes and missing data.
