Evolutionary biology Tools

Infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.

GADMA (Genetic Algorithm for Demographic Model Analysis) is a Python command-line tool that uses genetic algorithms and Bayesian optimization to automatically infer the demographic history of multiple populations from allele frequency spectrum (AFS) or VCF data, supporting up to three populations and multiple inference engines including dadi and moments.

A fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood. IQ-TREE compares favorably to RAxML and PhyML in terms of likelihoods with similar computing time

Adopts model selection and model averaging to calculate nonsynonymous (Ka) and synonymous (Ks) substitution rates, attempting to include as many features as needed for accurately capturing evolutionary information in protein-coding sequences. In addition, several existing methods for calculating Ka and Ks are also incorporated into KaKs_Calculator.

LDhat is a package written in the C and C++ languages for the analysis of recombination rates from population genetic data.

LTRpred is an R package for de novo annotation and prediction of LTR retrotransposons in genome sequences, using structural features and sequence homology to identify and classify LTR retrotransposon families.

Phylogenetic estimation software using Maximum Likelihood

The UShER toolkit includes a set of tools for for rapid, accurate placement of samples to existing phylogenies. While not restricted to SARS-CoV-2 phylogenetic analyses, it has enabled real-time phylogenetic analyses and genomic contact tracing in that its placement is orders of magnitude faster and more memory-efficient than previous methods.

BAYEsian genome SCAN for outliers, aims at identifying candidate loci under natural selection from genetic data, using differences in allele frequencies between populations. It is based on the multinomial-Dirichlet model.

fwdpy11 is a Python package for forward-time population genetic simulation, using a C++ back-end (fwdpp) for efficiency. It supports flexible modelling of selection, demography, and multiple populations, with custom temporal samplers for analyzing populations during simulation.