Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.

Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.

Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species...

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Autores principales: Seunghwa Kang, Jijun Tang, Stephen W Schaeffer, David A Bader
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2011
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Acceso en línea:https://doaj.org/article/b608c4dba0cd444c88584359235e9671
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spelling oai:doaj.org-article:b608c4dba0cd444c88584359235e96712021-11-18T06:47:26ZRec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.1932-620310.1371/journal.pone.0022483https://doaj.org/article/b608c4dba0cd444c88584359235e96712011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21887219/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way. We design a new DCM based on the spectral method and also develop the COGNAC (Comparing Orders of Genes using Novel Algorithms and high-performance Computers) software package. COGNAC uses the new DCM to reduce the phylogenetic tree search space and selects an output tree from the reduced search space based on the MP principle. We test the new DCM using gene order data and inversion distance. The new DCM not only reduces the number of candidate tree topologies but also excludes erroneous tree topologies which can be selected by original MP methods. Initial labeling of internal genomes affects the accuracy of MP methods using gene order data, and the new DCM enables more accurate initial labeling as well. COGNAC demonstrates superior accuracy as a consequence. We compare COGNAC with FastME and the combination of the state of the art DCM (Rec-I-DCM3) and GRAPPA. COGNAC clearly outperforms FastME in accuracy. COGNAC--using the new DCM--also reconstructs a much more accurate tree in significantly shorter time than GRAPPA with Rec-I-DCM3.Seunghwa KangJijun TangStephen W SchaefferDavid A BaderPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e22483 (2011)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Seunghwa Kang
Jijun Tang
Stephen W Schaeffer
David A Bader
Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
description Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way. We design a new DCM based on the spectral method and also develop the COGNAC (Comparing Orders of Genes using Novel Algorithms and high-performance Computers) software package. COGNAC uses the new DCM to reduce the phylogenetic tree search space and selects an output tree from the reduced search space based on the MP principle. We test the new DCM using gene order data and inversion distance. The new DCM not only reduces the number of candidate tree topologies but also excludes erroneous tree topologies which can be selected by original MP methods. Initial labeling of internal genomes affects the accuracy of MP methods using gene order data, and the new DCM enables more accurate initial labeling as well. COGNAC demonstrates superior accuracy as a consequence. We compare COGNAC with FastME and the combination of the state of the art DCM (Rec-I-DCM3) and GRAPPA. COGNAC clearly outperforms FastME in accuracy. COGNAC--using the new DCM--also reconstructs a much more accurate tree in significantly shorter time than GRAPPA with Rec-I-DCM3.
format article
author Seunghwa Kang
Jijun Tang
Stephen W Schaeffer
David A Bader
author_facet Seunghwa Kang
Jijun Tang
Stephen W Schaeffer
David A Bader
author_sort Seunghwa Kang
title Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
title_short Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
title_full Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
title_fullStr Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
title_full_unstemmed Rec-DCM-Eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
title_sort rec-dcm-eigen: reconstructing a less parsimonious but more accurate tree in shorter time.
publisher Public Library of Science (PLoS)
publishDate 2011
url https://doaj.org/article/b608c4dba0cd444c88584359235e9671
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AT jijuntang recdcmeigenreconstructingalessparsimoniousbutmoreaccuratetreeinshortertime
AT stephenwschaeffer recdcmeigenreconstructingalessparsimoniousbutmoreaccuratetreeinshortertime
AT davidabader recdcmeigenreconstructingalessparsimoniousbutmoreaccuratetreeinshortertime
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