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DLIGHT – Lateral Gene Transfer Detection Using Pairwise Evolutionary Distances in a Statistical Framework

  • Christophe Dessimoz
  • Daniel Margadant
  • Gaston H. Gonnet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)

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.

Keywords

Horizontal Gene Transfer Lateral Gene Trans Phylogenetic Method Sulfolobus Solfataricus Lateral Gene Trans Event 
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 2008

Authors and Affiliations

  • Christophe Dessimoz
    • 1
  • Daniel Margadant
    • 1
  • Gaston H. Gonnet
    • 1
  1. 1.ETH Zurich, Institute of Computational Science, CH-8092 Zurich and, Swiss Institute of Bioinformatics 

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