On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT

Autores
Goloboff, Pablo Augusto; Pol, Diego
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Roshan et al. recently described a ”divide-and-conquer” technique for parsimony analysis of large datasets, Rec-I-DCM3, and stated that it compares very favorably to results using the program TNT. Their technique is based on selecting subsets of taxa to create reduced datasets or subproblems, finding most-parsimonious trees for each reduced data set, recombining all parts together, and then performing global TBR swapping on the combined tree. Here, we contrast this approach to sectorial searches, a divide-and-conquer algorithm implemented in TNT. This algorithm also uses a guide tree to create subproblems, with the first-pass state sets of the nodes that join the selected sectors with the rest of the topology; this allows exact length calculations for the entire topology (that is, any solution N steps shorter than the original, for the reduced subproblem, must also be N steps shorter for the entire topology). We show here that, for sectors of similar size analyzed with the same search algorithms, subdividing datasets with sectorial searches produces better results than subdividing with Rec-I-DCM3. Roshan et al.’s claim that Rec-I-DCM3 outperforms thetechniques in TNT was caused by a poor experimental design and algorithmic settings used for the runs in TNT. In particular, for finding trees at or very close to the minimum known length of the analyzed datasets, TNT clearly outperforms Rec-I-DCM3. Finally, we show that the performance of Rec-I-DCM3 is bound by the efficiency of TBR implementation for the complete dataset, as this method behaves (after some number of iterations) as a technique for cyclic perturbations and improvements more than as a divide-and-conquer strategy.
Fil: Goloboff, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; Argentina
Fil: Pol, Diego. Museo Paleontológico Egidio Feruglio; Argentina
Materia
Phylogeny
Algorithms
Cladistics
Tree Searches
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/82978

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spelling On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNTGoloboff, Pablo AugustoPol, DiegoPhylogenyAlgorithmsCladisticsTree Searcheshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Roshan et al. recently described a ”divide-and-conquer” technique for parsimony analysis of large datasets, Rec-I-DCM3, and stated that it compares very favorably to results using the program TNT. Their technique is based on selecting subsets of taxa to create reduced datasets or subproblems, finding most-parsimonious trees for each reduced data set, recombining all parts together, and then performing global TBR swapping on the combined tree. Here, we contrast this approach to sectorial searches, a divide-and-conquer algorithm implemented in TNT. This algorithm also uses a guide tree to create subproblems, with the first-pass state sets of the nodes that join the selected sectors with the rest of the topology; this allows exact length calculations for the entire topology (that is, any solution N steps shorter than the original, for the reduced subproblem, must also be N steps shorter for the entire topology). We show here that, for sectors of similar size analyzed with the same search algorithms, subdividing datasets with sectorial searches produces better results than subdividing with Rec-I-DCM3. Roshan et al.’s claim that Rec-I-DCM3 outperforms thetechniques in TNT was caused by a poor experimental design and algorithmic settings used for the runs in TNT. In particular, for finding trees at or very close to the minimum known length of the analyzed datasets, TNT clearly outperforms Rec-I-DCM3. Finally, we show that the performance of Rec-I-DCM3 is bound by the efficiency of TBR implementation for the complete dataset, as this method behaves (after some number of iterations) as a technique for cyclic perturbations and improvements more than as a divide-and-conquer strategy.Fil: Goloboff, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; ArgentinaFil: Pol, Diego. Museo Paleontológico Egidio Feruglio; ArgentinaOxford University Press2007-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/82978Goloboff, Pablo Augusto; Pol, Diego; On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT; Oxford University Press; Systematic Biology; 56; 3; 12-2007; 485-4951063-5157CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/10635150701431905info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/sysbio/article-pdf/56/3/485/24203534/56-3-485.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:50:49Zoai:ri.conicet.gov.ar:11336/82978instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:50:49.48CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
title On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
spellingShingle On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
Goloboff, Pablo Augusto
Phylogeny
Algorithms
Cladistics
Tree Searches
title_short On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
title_full On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
title_fullStr On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
title_full_unstemmed On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
title_sort On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT
dc.creator.none.fl_str_mv Goloboff, Pablo Augusto
Pol, Diego
author Goloboff, Pablo Augusto
author_facet Goloboff, Pablo Augusto
Pol, Diego
author_role author
author2 Pol, Diego
author2_role author
dc.subject.none.fl_str_mv Phylogeny
Algorithms
Cladistics
Tree Searches
topic Phylogeny
Algorithms
Cladistics
Tree Searches
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Roshan et al. recently described a ”divide-and-conquer” technique for parsimony analysis of large datasets, Rec-I-DCM3, and stated that it compares very favorably to results using the program TNT. Their technique is based on selecting subsets of taxa to create reduced datasets or subproblems, finding most-parsimonious trees for each reduced data set, recombining all parts together, and then performing global TBR swapping on the combined tree. Here, we contrast this approach to sectorial searches, a divide-and-conquer algorithm implemented in TNT. This algorithm also uses a guide tree to create subproblems, with the first-pass state sets of the nodes that join the selected sectors with the rest of the topology; this allows exact length calculations for the entire topology (that is, any solution N steps shorter than the original, for the reduced subproblem, must also be N steps shorter for the entire topology). We show here that, for sectors of similar size analyzed with the same search algorithms, subdividing datasets with sectorial searches produces better results than subdividing with Rec-I-DCM3. Roshan et al.’s claim that Rec-I-DCM3 outperforms thetechniques in TNT was caused by a poor experimental design and algorithmic settings used for the runs in TNT. In particular, for finding trees at or very close to the minimum known length of the analyzed datasets, TNT clearly outperforms Rec-I-DCM3. Finally, we show that the performance of Rec-I-DCM3 is bound by the efficiency of TBR implementation for the complete dataset, as this method behaves (after some number of iterations) as a technique for cyclic perturbations and improvements more than as a divide-and-conquer strategy.
Fil: Goloboff, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; Argentina
Fil: Pol, Diego. Museo Paleontológico Egidio Feruglio; Argentina
description Roshan et al. recently described a ”divide-and-conquer” technique for parsimony analysis of large datasets, Rec-I-DCM3, and stated that it compares very favorably to results using the program TNT. Their technique is based on selecting subsets of taxa to create reduced datasets or subproblems, finding most-parsimonious trees for each reduced data set, recombining all parts together, and then performing global TBR swapping on the combined tree. Here, we contrast this approach to sectorial searches, a divide-and-conquer algorithm implemented in TNT. This algorithm also uses a guide tree to create subproblems, with the first-pass state sets of the nodes that join the selected sectors with the rest of the topology; this allows exact length calculations for the entire topology (that is, any solution N steps shorter than the original, for the reduced subproblem, must also be N steps shorter for the entire topology). We show here that, for sectors of similar size analyzed with the same search algorithms, subdividing datasets with sectorial searches produces better results than subdividing with Rec-I-DCM3. Roshan et al.’s claim that Rec-I-DCM3 outperforms thetechniques in TNT was caused by a poor experimental design and algorithmic settings used for the runs in TNT. In particular, for finding trees at or very close to the minimum known length of the analyzed datasets, TNT clearly outperforms Rec-I-DCM3. Finally, we show that the performance of Rec-I-DCM3 is bound by the efficiency of TBR implementation for the complete dataset, as this method behaves (after some number of iterations) as a technique for cyclic perturbations and improvements more than as a divide-and-conquer strategy.
publishDate 2007
dc.date.none.fl_str_mv 2007-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/82978
Goloboff, Pablo Augusto; Pol, Diego; On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT; Oxford University Press; Systematic Biology; 56; 3; 12-2007; 485-495
1063-5157
CONICET Digital
CONICET
url http://hdl.handle.net/11336/82978
identifier_str_mv Goloboff, Pablo Augusto; Pol, Diego; On divide-and-conquer strategies for parsimony analysis of large data sets: Rec-I-dcm3 vs. TNT; Oxford University Press; Systematic Biology; 56; 3; 12-2007; 485-495
1063-5157
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/10635150701431905
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/sysbio/article-pdf/56/3/485/24203534/56-3-485.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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