MetaPIGA 2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics

Raphaël Helaers, Michel Milinkovitch

Research output: Contribution to journalArticle


Results. Here, we present MetaPIGA2, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA2 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for selecting substitution models that best fit the data. MetaPIGA2 provides high parametrization, manual batch file and command line processing. However, it also offers an extensive graphical user interface for parameter setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA2 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and/or multicore computers. Conclusions. The metapopulation Genetic Algorithm resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Its implementation into a single software with additional stochastic heuristics will allow their rigorous optimization as well as a meaningful comparison of performances among these algorithms. MetaPIGA2 gives access both to high parameterization for the phylogeneticist, as well as to an ergonomic interface and functionalities assisting the non-specialist for sound inference of large phylogenetic trees using nucleotide sequences. MetaPIGA2 is freely available to academics at and .
Original languageEnglish
JournalBMC Bioinformatics
Publication statusUnpublished - 2010


  • Optimization
  • Stochastic Heurstics
  • Phylogeny Inference
  • Maximum Likelihood
  • Genetic Algorithm

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