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An approximate maximum likelihood approach, applied to phylogenetic trees

In English

Författare

Summary, in English

A novel type of approximation scheme to the maximum likelihood (ML) approach is presented and discussed in the context of phylogenetic tree reconstruction from aligned DNA sequences. It is based on a parameterized approximation to the conditional distribution of hidden variables (related, e.g., to the sequences of unobserved branch point ancestors) given the observed data. A modified likelihood, based on the extended data, is then maximized with respect to the parameters of the model as well as to those involved in the approximation. With a suitable form of the approximations the proposed method allows for simpler updating of the parameters, at the cost of an increased parameter count and a slight decrease in performance. The method is tested on phylogenetic tree reconstruction from artificially generated sequences, and its performance is compared to that of ML, showing that the approach is competitive for reasonably similar sequences. The method is also applied to real DNA sequences from primates, yielding a result consistent with those obtained by other standard algorithms.

Publiceringsår

2003

Språk

Engelska

Sidor

737-749

Publikation/Tidskrift/Serie

Journal of Computational Biology

Volym

10

Issue

5

Dokumenttyp

Artikel i tidskrift

Förlag

Mary Ann Liebert, Inc.

Ämne

  • Biophysics

Nyckelord

  • variational
  • phylogeny
  • maximum likelihood
  • mean-field

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1557-8666