Comparison of chosen features of the three software options.

<p>Notes:</p

Back to BaySICS: A User-Friendly Program for Bayesian Statistical Inference from Coalescent Simulations

<div><p>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.</p></div

Simulated examples employed for the performance assessment analysis.

<p>They were termed simulated example 1(A), simulated example 2 (B) and simulated example 3(C) in the text. A) The first scenario consists of a single change in population size; B) the second scenario consists of a bottleneck followed by population recovery; and C) the third scenario represents a population structure with three populations. The sampling consisted of 51 contemporary haploid individuals in (A); three samples of 17 haploid individuals each, which were taken at present, 15 000 and 35 000 generations before present, in (B); and three samples of 17 haploid individuals each, taken at present, 2 500 and 5 000 generations before present, in (C). The mutation rate was set to 0.15/nucleotide site/10⁶y, the transition/transversion bias was 0.875 and the gamma shape parameter was 0.15. The DNA sequences were l 000 bp long. Boxes show the parameters and prior densities which they were sampled from (<i>U</i>(<i>a</i>,<i>b</i>) means uniform distribution in the interval <i>a</i> to <i>b</i>).</p

Comparison of different standardization factors for the summary statistics.

<p>The charts at left show the probability distribution of four hypothetical summary statistics (before applying any rejection or adjustment) that were chosen to show the properties of six normalizing factors corresponding to the columns in the table at the right. For each summary statistic, three values (<i>x</i>₁, <i>x</i>₂, and <i>x</i>₃) were used to calculate the distances between them and the observed value and then to adjust those distances by the different normalizing factors. The three points, <i>x</i>₁, <i>x</i>₂, and <i>x</i>₃, were chosen to coincide with the quantiles of 2.5%, 50% (the median) and 97.5% respectively of the distributions of the summary statistics, in order to induce the behaviour of the gross of the simulated values. The table shows the values of <i>x</i>₁, <i>x</i>₂, and <i>x</i>₃ when standardized by the different normalizing factors and the bars below illustrate the proportional contribution of each summary statistic (identified by colour) to the overall composite Euclidean distance that would result if those summary statistics corresponded to the ones used for performing an hypothetical ABC analysis.</p

Delling et al 2014_Coregonus albula_microsatellite data

Microsatellite genotypes (9 loci) for 698 Coregonus albula individuals. See read me file for further details