Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes

  • Authors:

  • Daniel Barker;Andrew Meade;Mark Pagel

  • Affiliations:

  • Sir Harold Mitchell Building, School of Biology, University of St Andrews St Andrews, Fife, KY16 9TH, UK;School of Biological Sciences, University of Reading Whiteknights, Reading, RG6 6AJ, UK;School of Biological Sciences, University of Reading Whiteknights, Reading, RG6 6AJ, UK

  • Venue:
  • Bioinformatics
  • Year:
  • 2007

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Abstract

Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk Contact: m.pagel@rdg.ac.uk Supplementary information: Supplementary Data are available at Bioinformatics online.