Literature DB >> 22976081

Phylogenetics, likelihood, evolution and complexity.

A P Jason de Koning1, Wanjun Gu, Todd A Castoe, David D Pollock.   

Abstract

SUMMARY: Phylogenetics, likelihood, evolution and complexity (PLEX) is a flexible and fast Bayesian Markov chain Monte Carlo software program for large-scale analysis of nucleotide and amino acid data using complex evolutionary models in a phylogenetic framework. The program gains large speed improvements over standard approaches by implementing 'partial sampling of substitution histories', a data augmentation approach that can reduce data analysis times from months to minutes on large comparative datasets. A variety of nucleotide and amino acid substitution models are currently implemented, including non-reversible and site-heterogeneous mixture models. Due to efficient algorithms that scale well with data size and model complexity, PLEX can be used to make inferences from hundreds to thousands of taxa in only minutes on a desktop computer. It also performs probabilistic ancestral sequence reconstruction. Future versions will support detection of co-evolutionary interactions between sites, probabilistic tests of convergent evolution and rigorous testing of evolutionary hypotheses in a Bayesian framework.
AVAILABILITY AND IMPLEMENTATION: PLEX v1.0 is licensed under GPL. Source code and documentation will be available for download at www.evolutionarygenomics.com/ProgramsData/PLEX. PLEX is implemented in C++ and supported on Linux, Mac OS X and other platforms supporting standard C++ compilers. Example data, control files, documentation and accessory Perl scripts are available from the website. CONTACT: David.Pollock@UCDenver.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2012        PMID: 22976081      PMCID: PMC3496332          DOI: 10.1093/bioinformatics/bts555

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


  9 in total

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Authors:  Jeremiah J Faith; David D Pollock
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2.  MrBayes 3: Bayesian phylogenetic inference under mixed models.

Authors:  Fredrik Ronquist; John P Huelsenbeck
Journal:  Bioinformatics       Date:  2003-08-12       Impact factor: 6.937

3.  Conjugate Gibbs sampling for Bayesian phylogenetic models.

Authors:  Nicolas Lartillot
Journal:  J Comput Biol       Date:  2006-12       Impact factor: 1.479

4.  Computing Bayes factors using thermodynamic integration.

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Journal:  Syst Biol       Date:  2006-04       Impact factor: 15.683

5.  PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating.

Authors:  Nicolas Lartillot; Thomas Lepage; Samuel Blanquart
Journal:  Bioinformatics       Date:  2009-06-17       Impact factor: 6.937

6.  Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

Authors:  A P Jason de Koning; Wanjun Gu; David D Pollock
Journal:  Mol Biol Evol       Date:  2009-09-25       Impact factor: 16.240

7.  Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles.

Authors:  Nicolas Rodrigue; Hervé Philippe; Nicolas Lartillot
Journal:  Proc Natl Acad Sci U S A       Date:  2010-02-22       Impact factor: 11.205

8.  Fast Bayesian choice of phylogenetic models: prospecting data augmentation-based thermodynamic integration.

Authors:  Nicolas Rodrigue; Stéphane Aris-Brosou
Journal:  Syst Biol       Date:  2011-07-29       Impact factor: 15.683

9.  BEAST: Bayesian evolutionary analysis by sampling trees.

Authors:  Alexei J Drummond; Andrew Rambaut
Journal:  BMC Evol Biol       Date:  2007-11-08       Impact factor: 3.260

  9 in total
  5 in total

1.  FUBAR: a fast, unconstrained bayesian approximation for inferring selection.

Authors:  Ben Murrell; Sasha Moola; Amandla Mabona; Thomas Weighill; Daniel Sheward; Sergei L Kosakovsky Pond; Konrad Scheffler
Journal:  Mol Biol Evol       Date:  2013-02-18       Impact factor: 16.240

2.  Nonadaptive Amino Acid Convergence Rates Decrease over Time.

Authors:  Richard A Goldstein; Stephen T Pollard; Seena D Shah; David D Pollock
Journal:  Mol Biol Evol       Date:  2015-03-03       Impact factor: 16.240

3.  State aggregation for fast likelihood computations in molecular evolution.

Authors:  Iakov I Davydov; Marc Robinson-Rechavi; Nicolas Salamin
Journal:  Bioinformatics       Date:  2017-02-01       Impact factor: 6.937

4.  The tangled bank of amino acids.

Authors:  Richard A Goldstein; David D Pollock
Journal:  Protein Sci       Date:  2016-05-12       Impact factor: 6.725

5.  Inferring the number and position of changes in selective regime in a non-equilibrium mutation-selection framework.

Authors:  Andrew M Ritchie; Tristan L Stark; David A Liberles
Journal:  BMC Ecol Evol       Date:  2021-03-10
  5 in total

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