Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations.

Edson Sandoval-Castellanos,Love Dalén,Eleftheria Palkopoulou,David Caramelli

doi:10.1371/journal.pone.0098011

Edson Sandoval-Castellanos, Love Dalén + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0098011

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Journal: PloS one	Publication Date: May 27, 2014
Citations: 46	License type: CC BY 4.0

Affiliation: Swedish Museum of Natural History, Stockholm University

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.

Highlights

The power of population genetics and genomics to infer past evolutionary processes has vastly increased in the last 30 years due to the synergy created by highly influential advances, both theoretical (coalescent theory, Bayesian statistics) and technological (high throughput sequencing, high performance computers, ancient DNA analysis) [1], [2], [3].While coalescent theory has created a simple, powerful, and elegant way to model evolutionary processes [1], [4], Bayesian statistics have provided a solid theoretical framework for the treatment of complex systems as well as for inference based on computer simulations [5], [6], [7]
The power of population genetics and genomics to infer past evolutionary processes has vastly increased in the last 30 years due to the synergy created by highly influential advances, both theoretical and technological [1], [2], [3]
BaySICS implements a number of tools for improving the simulations and interpretation of results, including novel summary statistics specific for ancient DNA data, 2-D and 3-D graphics, as well as an Markov chain Monte Carlo (MCMC)-without-likelihoods algorithm

Summary

Introduction

The power of population genetics and genomics to infer past evolutionary processes has vastly increased in the last 30 years due to the synergy created by highly influential advances, both theoretical (coalescent theory, Bayesian statistics) and technological (high throughput sequencing, high performance computers, ancient DNA analysis) [1], [2], [3].While coalescent theory has created a simple, powerful, and elegant way to model evolutionary processes [1], [4], Bayesian statistics have provided a solid theoretical framework for the treatment of complex systems as well as for inference based on computer simulations [5], [6], [7]. A Windows program that provides an integrated and user-friendly platform to perform coalescent simulations for DNA sequence data and ABC analysis including estimation of posterior densities for population parameters and Bayes factors for model comparisons.

Results

Conclusion