Reducing model complexity of the general Markov model of evolution

Vivek Jayaswal, Faisal Ababneh, Lars S. Jermiin, John Robinson

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model. The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.

Original languageEnglish
Pages (from-to)3045-3059
Number of pages15
JournalMolecular Biology and Evolution
Volume28
Issue number11
DOIs
Publication statusPublished - Nov 2011

Fingerprint

Molecular Models
data analysis
heuristics
Heuristics
phylogenetics
phylogeny

Keywords

  • general Markov models
  • model complexity
  • model selection
  • parameter estimation
  • phylogenetics

ASJC Scopus subject areas

  • Medicine(all)
  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

Cite this

Reducing model complexity of the general Markov model of evolution. / Jayaswal, Vivek; Ababneh, Faisal; Jermiin, Lars S.; Robinson, John.

In: Molecular Biology and Evolution, Vol. 28, No. 11, 11.2011, p. 3045-3059.

Research output: Contribution to journalArticle

Jayaswal, Vivek ; Ababneh, Faisal ; Jermiin, Lars S. ; Robinson, John. / Reducing model complexity of the general Markov model of evolution. In: Molecular Biology and Evolution. 2011 ; Vol. 28, No. 11. pp. 3045-3059.
@article{388ac2bd064e451f976d0079cc52d62a,
title = "Reducing model complexity of the general Markov model of evolution",
abstract = "The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model. The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.",
keywords = "general Markov models, model complexity, model selection, parameter estimation, phylogenetics",
author = "Vivek Jayaswal and Faisal Ababneh and Jermiin, {Lars S.} and John Robinson",
year = "2011",
month = "11",
doi = "10.1093/molbev/msr128",
language = "English",
volume = "28",
pages = "3045--3059",
journal = "Molecular Biology and Evolution",
issn = "0737-4038",
publisher = "Oxford University Press",
number = "11",

}

TY - JOUR

T1 - Reducing model complexity of the general Markov model of evolution

AU - Jayaswal, Vivek

AU - Ababneh, Faisal

AU - Jermiin, Lars S.

AU - Robinson, John

PY - 2011/11

Y1 - 2011/11

N2 - The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model. The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.

AB - The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model. The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.

KW - general Markov models

KW - model complexity

KW - model selection

KW - parameter estimation

KW - phylogenetics

UR - http://www.scopus.com/inward/record.url?scp=80155187375&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80155187375&partnerID=8YFLogxK

U2 - 10.1093/molbev/msr128

DO - 10.1093/molbev/msr128

M3 - Article

VL - 28

SP - 3045

EP - 3059

JO - Molecular Biology and Evolution

JF - Molecular Biology and Evolution

SN - 0737-4038

IS - 11

ER -