• DocumentCode
    1436310
  • Title

    Markov Invariants for Phylogenetic Rate Matrices Derived from Embedded Submodels

  • Author

    Jarvis, Peter D. ; Sumner, Jeremy G.

  • Author_Institution
    Sch. of Math. & Phys., Univ. of Tasmania, Hobart Tas, TAS, Australia
  • Volume
    9
  • Issue
    3
  • fYear
    2012
  • Firstpage
    828
  • Lastpage
    836
  • Abstract
    We consider novel phylogenetic models with rate matrices that arise via the embedding of a progenitor model on a small number of character states, into a target model on a larger number of character states. Adapting representation-theoretic results from recent investigations of Markov invariants for the general rate matrix model, we give a prescription for identifying and counting Markov invariants for such "symmetric embedded” models, and we provide enumerations of these for the first few cases with a small number of character states. The simplest example is a target model on three states, constructed from a general 2 state model; the "2 hookrightarrow 3” embedding. We show that for 2 taxa, there exist two invariants of quadratic degree that can be used to directly infer pairwise distances from observed sequences under this model. A simple simulation study verifies their theoretical expected values, and suggests that, given the appropriateness of the model class, they have superior statistical properties than the standard (log) Det invariant (which is of cubic degree for this case).
  • Keywords
    M-theory; Markov processes; embedded systems; evolution (biological); genetics; physiological models; statistical analysis; Markov invariants; general rate matrix model; phylogenetic rate matrices; progenitor model; standard det invariant; statistical properties; symmetric embedded models; Adaptation models; Algebra; Biological system modeling; Markov processes; Phylogeny; Polynomials; Tensile stress; Markov chains; representation theory.; Markov Chains; Models, Genetic; Phylogeny;
  • fLanguage
    English
  • Journal_Title
    Computational Biology and Bioinformatics, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5963
  • Type

    jour

  • DOI
    10.1109/TCBB.2012.24
  • Filename
    6143916