PTree: pattern-based, stochastic search for maximum parsimony phylogenies
Phylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is o...
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doaj-59342967a1c642fdb1e31feaea44f3242020-11-24T23:24:22ZengPeerJ Inc.PeerJ2167-83592013-06-011e8910.7717/peerj.8989PTree: pattern-based, stochastic search for maximum parsimony phylogeniesIvan Gregor0Lars Steinbrück1Alice C. McHardy2Max-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, Saarbrücken, GermanyDepartment of Algorithmic Bioinformatics, Heinrich-Heine-University Düsseldorf, Düsseldorf, GermanyMax-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, Saarbrücken, GermanyPhylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. Here, we describe a stochastic search method for a maximum parsimony tree, implemented in a software package we named PTree. Our method is based on a new pattern-based technique that enables us to infer intermediate sequences efficiently where the incorporation of these sequences in the current tree topology yields a phylogenetic tree with a lower cost. Evaluation across multiple datasets showed that our method is comparable to the algorithms implemented in PAUP* or TNT, which are widely used by the bioinformatics community, in terms of topological accuracy and runtime. We show that our method can process large-scale datasets of 1,000–8,000 sequences. We believe that our novel pattern-based method enriches the current set of tools and methods for phylogenetic tree inference. The software is available under: http://algbio.cs.uni-duesseldorf.de/webapps/wa-download/.https://peerj.com/articles/89.pdfPhylogeny reconstructionMaximum parsimonyLocal searchStochastic search |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ivan Gregor Lars Steinbrück Alice C. McHardy |
spellingShingle |
Ivan Gregor Lars Steinbrück Alice C. McHardy PTree: pattern-based, stochastic search for maximum parsimony phylogenies PeerJ Phylogeny reconstruction Maximum parsimony Local search Stochastic search |
author_facet |
Ivan Gregor Lars Steinbrück Alice C. McHardy |
author_sort |
Ivan Gregor |
title |
PTree: pattern-based, stochastic search for maximum parsimony phylogenies |
title_short |
PTree: pattern-based, stochastic search for maximum parsimony phylogenies |
title_full |
PTree: pattern-based, stochastic search for maximum parsimony phylogenies |
title_fullStr |
PTree: pattern-based, stochastic search for maximum parsimony phylogenies |
title_full_unstemmed |
PTree: pattern-based, stochastic search for maximum parsimony phylogenies |
title_sort |
ptree: pattern-based, stochastic search for maximum parsimony phylogenies |
publisher |
PeerJ Inc. |
series |
PeerJ |
issn |
2167-8359 |
publishDate |
2013-06-01 |
description |
Phylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. Here, we describe a stochastic search method for a maximum parsimony tree, implemented in a software package we named PTree. Our method is based on a new pattern-based technique that enables us to infer intermediate sequences efficiently where the incorporation of these sequences in the current tree topology yields a phylogenetic tree with a lower cost. Evaluation across multiple datasets showed that our method is comparable to the algorithms implemented in PAUP* or TNT, which are widely used by the bioinformatics community, in terms of topological accuracy and runtime. We show that our method can process large-scale datasets of 1,000–8,000 sequences. We believe that our novel pattern-based method enriches the current set of tools and methods for phylogenetic tree inference. The software is available under: http://algbio.cs.uni-duesseldorf.de/webapps/wa-download/. |
topic |
Phylogeny reconstruction Maximum parsimony Local search Stochastic search |
url |
https://peerj.com/articles/89.pdf |
work_keys_str_mv |
AT ivangregor ptreepatternbasedstochasticsearchformaximumparsimonyphylogenies AT larssteinbruck ptreepatternbasedstochasticsearchformaximumparsimonyphylogenies AT alicecmchardy ptreepatternbasedstochasticsearchformaximumparsimonyphylogenies |
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1725561076991393792 |