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Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice

The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice imple... Full description

Journal Title: Systematic Biology 2013-01-01, Vol.62 (1), p.162-166
Main Author: Aberer, Andre J
Other Authors: Krompass, Denis , Stamatakis, Alexandros
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Oxford University Press
ID: ISSN: 1063-5157
Link: https://www.ncbi.nlm.nih.gov/pubmed/22962004
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3526802
title: Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice
format: Article
creator:
  • Aberer, Andre J
  • Krompass, Denis
  • Stamatakis, Alexandros
subjects:
  • Algorithms
  • Bioinformatics
  • Bootstrap support
  • Branches
  • Classification - methods
  • Comparative analysis
  • Computer Simulation
  • consensus tree
  • Datasets
  • Evolution
  • Identification
  • Inference
  • Internet
  • phylogenetic postanalysis
  • Phylogenetics
  • Phylogeny
  • Prunes
  • Pruning
  • Reproducibility of Results
  • rogue taxa
  • Simulation
  • Software
  • Software for Systematics
  • Software for Systematics and Evolution
  • Taxa
  • Topology
  • webservice
ispartof: Systematic Biology, 2013-01-01, Vol.62 (1), p.162-166
description: The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1063-5157
fulltext: fulltext
issn:
  • 1063-5157
  • 1076-836X
url: Link


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descriptionThe presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.
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subjectAlgorithms ; Bioinformatics ; Bootstrap support ; Branches ; Classification - methods ; Comparative analysis ; Computer Simulation ; consensus tree ; Datasets ; Evolution ; Identification ; Inference ; Internet ; phylogenetic postanalysis ; Phylogenetics ; Phylogeny ; Prunes ; Pruning ; Reproducibility of Results ; rogue taxa ; Simulation ; Software ; Software for Systematics ; Software for Systematics and Evolution ; Taxa ; Topology ; webservice
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descriptionThe presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.
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abstractThe presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.
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