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Parametric Analysis for Ungapped Markov Models of Evolution

  • David Fernández-Baca
  • Balaji Venkatachalam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3537)

Abstract

We present efficient sensitivity-analysis algorithms for two problems involving Markov models of sequence evolution: ancestral reconstruction in evolutionary trees and local ungapped alignment under log-odds scoring. Our algorithms generate complete descriptions of the optimum solutions for all possible values of the evolutionary distance. The running time for the parametric ancestral reconstruction problem under the Kimura 2-parameter model is O(kn + kn 2/3 log k), where n is the number of sequences and k is their length, assuming all edges have the same length. For the parametric gapless alignment problem under the Jukes-Cantor model, the running time is O(mn + mn 2/3 log m), where m and n are the sequence lengths and nm.

Keywords

Markov Model Evolutionary Distance Sequence Evolution Parametric Problem Optimality Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David Fernández-Baca
    • 1
  • Balaji Venkatachalam
    • 1
  1. 1.Department of Computer ScienceIowa State UniversityAmesUSA

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