Bacmeta : simulator for genomic evolution in bacterial metapopulations

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http://hdl.handle.net/10138/303631

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Sipola , A , Marttinen , P & Corander , J 2018 , ' Bacmeta : simulator for genomic evolution in bacterial metapopulations ' , Bioinformatics , vol. 34 , no. 13 , pp. 2308-2310 . https://doi.org/10.1093/bioinformatics/bty093

Titel: Bacmeta : simulator for genomic evolution in bacterial metapopulations
Författare: Sipola, Aleksi; Marttinen, Pekka; Corander, Jukka
Medarbetare: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Jukka Corander / Principal Investigator
Datum: 2018-07-01
Språk: eng
Sidantal: 3
Tillhör serie: Bioinformatics
ISSN: 1367-4803
Permanenta länken (URI): http://hdl.handle.net/10138/303631
Abstrakt: The advent of genomic data from densely sampled bacterial populations has created a need for flexible simulators by which models and hypotheses can be efficiently investigated in the light of empirical observations. Bacmeta provides fast stochastic simulation of neutral evolution within a large collection of interconnected bacterial populations with completely adjustable connectivity network. Stochastic events of mutations, recombinations, insertions/deletions, migrations and micro-epidemics can be simulated in discrete non-overlapping generations with a Wright-Fisher model that operates on explicit sequence data of any desired genome length. Each model component, including locus, bacterial strain, population and ultimately the whole metapopulation, is efficiently simulated using C++ objects and detailed metadata from each level can be acquired. The software can be executed in a cluster environment using simple textual input files, enabling, e.g. large-scale simulations and likelihood-free inference. Availability and implementation: Bacmeta is implemented with C++ for Linux, Mac and Windows. It is available at https://bitbucket.org/aleksisipola/bacmeta under the BSD 3-clause license. Contact: aleksi.sipola@helsinki.fi or jukka.corander@medisin.uio.no Supplementary information: Supplementary data are available at Bioinformatics online.
Subject: APPROXIMATE BAYESIAN COMPUTATION
POPULATION-GENETICS
RECOMBINATION
COALESCENT
MODELS
1183 Plant biology, microbiology, virology
112 Statistics and probability
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