A weighted least-squares approach for inferring phylogenies from incomplete distance matrices

Bioinformatics. 2004 Sep 1;20(13):2113-21. doi: 10.1093/bioinformatics/bth211. Epub 2004 Apr 1.

Abstract

Motivation: The problem of phylogenetic inference from datasets including incomplete or uncertain entries is among the most relevant issues in systematic biology. In this paper, we propose a new method for reconstructing phylogenetic trees from partial distance matrices. The new method combines the usage of the four-point condition and the ultrametric inequality with a weighted least-squares approximation to solve the problem of missing entries. It can be applied to infer phylogenies from evolutionary data including some missing or uncertain information, for instance, when observed nucleotide or protein sequences contain gaps or missing entries.

Results: In a number of simulations involving incomplete datasets, the proposed method outperformed the well-known Ultrametric and Additive procedures. Generally, the new method also outperformed all the other competing approaches including Triangle and Fitch which is the most popular least-squares method for reconstructing phylogenies. We illustrate the usefulness of the introduced method by analyzing two well-known phylogenies derived from complete mammalian mtDNA sequences. Some interesting theoretical results concerning the NP-hardness of the ordinary and weighted least-squares fitting of a phylogenetic tree to a partial distance matrix are also established.

Availability: The T-Rex package including this method is freely available for download at http://www.info.uqam.ca/~makarenv/trex.html

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Chromosome Mapping / methods*
  • Evolution, Molecular
  • Genome
  • Least-Squares Analysis
  • Models, Genetic*
  • Models, Statistical*
  • Phylogeny*
  • Sequence Alignment / methods*
  • Sequence Analysis, DNA / methods*
  • Software