Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons.
Offers a solution to choose among different models of nucleotide substitution. jModelTest includes five different model selection strategies, including hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a performance-based decision theory method (DT). This tool assists users to monitor their jobs, displaying information about their current state and resources consumed.
A bioinformatic tool for the selection of best-fit models of amino acid replacement for the data at hand. ProtTest makes this selection by finding the model in the candidate list with the smallest Akaike information criterion (AIC), Bayesian information criterion (BIC) score or decision theory criterion (DT). At the same time, ProtTest obtains model-averaged estimates of different parameters (including a model-averaged phylogenetic tree) and calculates their importance. ProtTest 3 can be executed in parallel in HPC environments as: (i) a GUI-based desktop version that uses multicore processors; (ii) a cluster-based version that distributes the computational load among nodes; and (iii) as a hybrid multicore cluster version that achieves speed through the distribution of tasks among nodes while taking advantage of multicore processors within nodes.
Designs for rapid phylogenetic model selection on protein coding genes. ModelOMatic is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines.
A Java library that lets users use text to quickly and efficiently define novel forms of discrete data and create new substitution models that describe how those data change on a phylogeny. GeLL allows users to define general substitution models and data structures in a way that is not possible in other existing libraries, including mixture models and non-reversible models. Classes are provided for calculating likelihoods, optimizing model parameters and branch lengths, ancestral reconstruction and sequence simulation.
Selects optimal amino acid and nucleotide substitution models from Fasta or Phylip alignments. ModelGenerator supports 56 nucleotide and 96 amino acid substitution models. It uses the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC) and hierarchical Likelihood-ratio tests (hLRTs) to select the best-fitting model of substitution. ModelGenerator uses the PAL library to perform its calculations and is part of the popular wEMBOSS package.
Provides sequence retrieval, primer design, alignment, phylogeny reconstruction from a single toolkit. phylo-node is a Node.js toolkit that offers simple customization and portability options through various inheritance patterns. This method can be easily employed to develop complex but consistent workflows, and integrated with existing bioinformatics tools using the Node.js codebase to support the production of diverse pipelines.
Determines the best-fit model of evolution for DNA and protein alignments. ModelTest-NG integrates five different model selection strategies: hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a decision theory method (DT). It can be used to operate statistical selection of best-fit models of nucleotide substitution or amino acid replacement.
Enables model selection and model averaging. MuMIn is an R package that permits users to streamline information-theoretic model selection and execute model averaging based on the information criteria. The software contains functions such as “dredge” for performing automated model selection with subsets of the supplied ‘global’ model, “AICc” for calculation of second-order Akaike information criterion or others functions which can be used for standardizing data and model coefficients by Standard Deviation or Partial Standard Deviation.
Uses linear invariants defining the spaces of all phylogenetic mixtures under a given model. SPIn is a novel approach to select an evolutionary model in phylogenetics. This tool requires no input tree and is designed to deal with nonhomogeneous phylogenetic data consisting of multiple sequence alignments showing different patterns of evolution, for example, concatenated genes, exons, and/or introns. It successfully recovers the underlying evolutionary model and is shown to perform better than existing approaches.