Open AccessBack to BaySICS: A User-Friendly Program for Bayesian Statistical Inference from Coalescent Simulations
Author(s) -
Edson SandovalCastellanos,
Eleftheria Palkopoulou,
Love Dalén
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0098011
Subject(s) - approximate bayesian computation , coalescent theory , inference , computer science , bayes' theorem , markov chain monte carlo , flexibility (engineering) , population , bayesian probability , statistical inference , bayesian inference , machine learning , bayes factor , data mining , statistical model , artificial intelligence , statistics , mathematics , biology , gene , phylogenetic tree , biochemistry , demography , sociology
Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.