MPI-PHYLIP: parallelizing computationally intensive phylogenetic analysis routines for the analysis of large protein families

• Published in: PloS one Vol. 5; no. 11; p. e13999
• Main Authors: Ropelewski, Alexander J, Nicholas, Hugh B, Gonzalez Mendez, Ricardo R
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.11.2010; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Biochemistry/Bioinformatics; Biochemistry/Molecular Evolution; Bioinformatics; Cladistic analysis; Computational Biology - methods; Computational Biology/Macromolecular Sequence Analysis; Computer memory; Computer Science/Applications; Data collection; Datasets; Dehydrogenases; Epidemiology; Evolution; Evolutionary Biology/Bioinformatics; Gene sequencing; Genomes; Humans; Identification; Information theory; International conferences; Mathematical analysis; Parallel processing; Phylogenetics; Phylogeny; Protein families; Proteins; Proteins - classification; Proteins - genetics; Reproducibility of Results; Resampling; Routines; Sequence Alignment - methods; Simulation; Software; United States > US; Pittsburgh Pennsylvania; Pennsylvania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0013999

Phylogenetic study of protein sequences provides unique and valuable insights into the molecular and genetic basis of important medical and epidemiological problems as well as insights about the origins and development of physiological features in present day organisms. Consensus phylogenies based on the bootstrap and other resampling methods play a crucial part in analyzing the robustness of the trees produced for these analyses. Our focus was to increase the number of bootstrap replications that can be performed on large protein datasets using the maximum parsimony, distance matrix, and maximum likelihood methods. We have modified the PHYLIP package using MPI to enable large-scale phylogenetic study of protein sequences, using a statistically robust number of bootstrapped datasets, to be performed in a moderate amount of time. This paper discusses the methodology used to parallelize the PHYLIP programs and reports the performance of the parallel PHYLIP programs that are relevant to the study of protein evolution on several protein datasets. Calculations that currently take a few days on a state of the art desktop workstation are reduced to calculations that can be performed over lunchtime on a modern parallel computer. Of the three protein methods tested, the maximum likelihood method scales the best, followed by the distance method, and then the maximum parsimony method. However, the maximum likelihood method requires significant memory resources, which limits its application to more moderately sized protein datasets.