MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics
Open Access
- 15 July 2010
- journal article
- research article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 11 (1) , 379
- https://doi.org/10.1186/1471-2105-11-379
Abstract
The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities.Keywords
This publication has 42 references indexed in Scilit:
- New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0Systematic Biology, 2010
- Many-core algorithms for statistical phylogeneticsBioinformatics, 2009
- Large-scale assignment of orthology: back to phylogenetics?Genome Biology, 2008
- RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed modelsBioinformatics, 2006
- Assessing the Accuracy of Ancestral Protein Reconstruction MethodsPLoS Computational Biology, 2006
- Reconstructing large regions of an ancestral mammalian genome in silicoGenome Research, 2004
- The Effect of Haplotype-Block Definitions on Inference of Haplotype-Block Structure and htSNPs SelectionMolecular Biology and Evolution, 2004
- Among-site rate variation and its impact on phylogenetic analysesTrends in Ecology & Evolution, 1996
- Unlikelihood that minimal phylogenies for a realistic biological study can be constructed in reasonable computational timeMathematical Biosciences, 1982
- Evolutionary trees from DNA sequences: A maximum likelihood approachJournal of Molecular Evolution, 1981