title: Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations.
abstract: 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.
authors.affiliation: Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden; Department of Zoology, Stockholm University, Stockholm, Sweden.
authors.forename: Edson
authors.initials: E
authors.lastname: Sandoval-Castellanos
authors.affiliation: Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden; Department of Zoology, Stockholm University, Stockholm, Sweden.
authors.forename: Eleftheria
authors.initials: E
authors.lastname: Palkopoulou
authors.affiliation: Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.
authors.forename: Love
authors.initials: L
authors.lastname: Dalén
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D005828:Genetics, Population; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D044382:Population Groups / Q000235:genetics; D012984:Software