Identifying and reconstructing lateral transfers from distance matrices by combing the minimum contradiction method and neighbor-net

Thuillard, Marc and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 (2011) Identifying and reconstructing lateral transfers from distance matrices by combing the minimum contradiction method and neighbor-net. Journal of Bioinformatics and Computational Biology, 9 (4). pp. 453-470. ISSN 0219-7200

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Abstract

Identifying lateral gene transfers is an important problem in evolutionary biology. Under a simple model of evolution, the expected values of an evolutionary distance matrix describing a phylogenetic tree fulfill the so-called Kalmanson inequalities. The Minimum Contradiction method for identifying lateral gene transfers exploits the fact that lateral transfers may generate large deviations from the Kalmanson inequalities. Here a new approach is presented to deal with such cases that combines the Neighbor-Net algorithm for computing phylogenetic networks with the Minimum Contradiction method. A subset of taxa, prescribed using Neighbor-Net, is obtained by measuring how closely the Kalmanson inequalities are fulfilled by each taxon. A criterion is then used to identify the taxa, possibly involved in a lateral transfer between nonconsecutive taxa. We illustrate the utility of the new approach by applying it to a distance matrix for Archaea, Bacteria, and Eukaryota.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018) Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Users 2731 not found.
Date Deposited:	18 Oct 2011 13:43
Last Modified:	15 Jun 2023 23:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/35049
DOI:	10.1142/S0219720011005409

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