Literature DB >> 15513992

DPRml: distributed phylogeny reconstruction by maximum likelihood.

T M Keane1, T J Naughton, S A A Travers, J O McInerney, G P McCormack.   

Abstract

MOTIVATION: In recent years there has been increased interest in producing large and accurate phylogenetic trees using statistical approaches. However for a large number of taxa, it is not feasible to construct large and accurate trees using only a single processor. A number of specialized parallel programs have been produced in an attempt to address the huge computational requirements of maximum likelihood. We express a number of concerns about the current set of parallel phylogenetic programs which are currently severely limiting the widespread availability and use of parallel computing in maximum likelihood-based phylogenetic analysis.
RESULTS: We have identified the suitability of phylogenetic analysis to large-scale heterogeneous distributed computing. We have completed a distributed and fully cross-platform phylogenetic tree building program called distributed phylogeny reconstruction by maximum likelihood. It uses an already proven maximum likelihood-based tree building algorithm and a popular phylogenetic analysis library for all its likelihood calculations. It offers one of the most extensive sets of DNA substitution models currently available. We are the first, to our knowledge, to report the completion of a distributed phylogenetic tree building program that can achieve near-linear speedup while only using the idle clock cycles of machines. For those in an academic or corporate environment with hundreds of idle desktop machines, we have shown how distributed computing can deliver a 'free' ML supercomputer.

Mesh:

Year:  2004        PMID: 15513992     DOI: 10.1093/bioinformatics/bti100

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

1.  Testing for differentiation of microbial communities using phylogenetic methods: accounting for uncertainty of phylogenetic inference and character state mapping.

Authors:  Ryan T Jones; Andrew P Martin
Journal:  Microb Ecol       Date:  2006-07-07       Impact factor: 4.552

2.  Many-core algorithms for statistical phylogenetics.

Authors:  Marc A Suchard; Andrew Rambaut
Journal:  Bioinformatics       Date:  2009-04-15       Impact factor: 6.937

3.  Unifying vertical and nonvertical evolution: a stochastic ARG-based framework.

Authors:  Erik W Bloomquist; Marc A Suchard
Journal:  Syst Biol       Date:  2009-11-09       Impact factor: 15.683

4.  Phylogenetic inference via sequential Monte Carlo.

Authors:  Alexandre Bouchard-Côté; Sriram Sankararaman; Michael I Jordan
Journal:  Syst Biol       Date:  2012-01-04       Impact factor: 15.683

5.  PALM: a paralleled and integrated framework for phylogenetic inference with automatic likelihood model selectors.

Authors:  Shu-Hwa Chen; Sheng-Yao Su; Chen-Zen Lo; Kuei-Hsien Chen; Teng-Jay Huang; Bo-Han Kuo; Chung-Yen Lin
Journal:  PLoS One       Date:  2009-12-07       Impact factor: 3.240

6.  FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods.

Authors:  Stephanie Zierke; Jason D Bakos
Journal:  BMC Bioinformatics       Date:  2010-04-12       Impact factor: 3.169

7.  Distribution of megaplasmids in Lactobacillus salivarius and other lactobacilli.

Authors:  Yin Li; Carlos Canchaya; Fang Fang; Emma Raftis; Kieran A Ryan; Jan-Peter van Pijkeren; Douwe van Sinderen; Paul W O'Toole
Journal:  J Bacteriol       Date:  2007-06-22       Impact factor: 3.490

8.  Analysis of Acropora muricata calmodulin (CaM) indicates that scleractinian corals possess the ancestral exon/intron organization of the eumetazoan CaM gene.

Authors:  Chih-Yung Chiou; I-Ping Chen; Chienhsun Chen; Henry Ju-Lin Wu; Nuwei Vivian Wei; Carden C Wallace; Chaolun Allen Chen
Journal:  J Mol Evol       Date:  2008-03-06       Impact factor: 2.395

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

Authors:  Alexander J Ropelewski; Hugh B Nicholas; Ricardo R Gonzalez Mendez
Journal:  PLoS One       Date:  2010-11-15       Impact factor: 3.240

10.  Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system.

Authors:  Andrew J Page; Thomas M Keane; Thomas J Naughton
Journal:  J Parallel Distrib Comput       Date:  2010-07       Impact factor: 3.734

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