DLIGHT: lateral gene transfer detection using pairwise evolutionary distances in a statistical framework

  • Authors:
  • Christophe Dessimoz;Daniel Margadant;Gaston H. Gonnet

  • Affiliations:
  • ETH Zurich, Institute of Computational Science, Zurich and Swiss Institute of Bioinformatics;ETH Zurich, Institute of Computational Science, Zurich and Swiss Institute of Bioinformatics;ETH Zurich, Institute of Computational Science, Zurich and Swiss Institute of Bioinformatics

  • Venue:
  • RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
  • Year:
  • 2008

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Abstract

This paper presents an algorithm to detect lateral gene transfer (LGT) on the basis of pairwise evolutionary distances. The prediction is made from a likelihood ratio derived from hypotheses of LGT versus no LGT, using multivariate normal theory. In contrast to approaches based on explicit phylogenetic LGT detection, it avoids the high computational cost and pitfalls associated with gene tree inference, while maintaining the high level of characterization obtainable from such methods (species involved in LGT, direction, distance to the LGT event in the past). We validate the algorithm empirically using both simulation and real data, and compare its predictions with standard methods and other studies.