Software
Bait-ER
A fully Bayesian approach to estimate selection coefficients from Evolve & Resequence time series data.
Reference: Barata, Borges and Kosiol, Bait-ER: a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments, bioRxiv, 2020,
GPrank R package
R package for ranking genomic time series using Gaussian Process models. The package allows for finding potential selection targets in Evolve & Resequence experiments.
References: Topa and Honkela, Analysis of differential splicing suggests different modes of short-term splicing regulation, Bioinformatics, 2016, doi.org/10.1093/bioinformatics/btw283
Topa, Jonas, Kofler, Kosiol and Honkela, Gaussian process test for high-throughput sequencing time series: application to experimental evolution, Bioinformatics, 2015, doi.org/10.1093/bioinformatics/btv014
IQ-TREE PoMo
Polymorphism-aware phylogenetic Models (PoMo) applied to phylogenetic tree inference using population data (site frequency data). One may reconstruct a maximum-likelihood tree from alignment data using IQ-TREE PoMo.
Reference: Schrempf, Minh, De Maio, von Haeseler, and Kosiol, Reversible polymorphism-aware phylogenetic models and their application to tree inference, Journal of Theoretical Biology, 2016, doi.org/10.1016/j.jtbi.2016.07.042
RevBayes PoMo
The PoMo models’ full-likelihood approach also allows for Bayesian inference of species trees.
Includes tutorials on how to run PoMo on RevBayes.
References: Borges, Boussau, Szöllősi and Kosiol, Pervasive selection biases inferences of the species tree, bioRxiv, 2020,
Borges, Szöllősi and Kosiol, Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models, Genetics, 2019, doi.org/10.1534/genetics.119.302074