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SUMMARY:Identifying Lateral Transfers with Neighbor-Nets - Thuillard\, M (
 East Anglia)
DTSTART:20110620T145000Z
DTEND:20110620T151000Z
UID:TALK31792@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:With the ever increasing availability of complete genomes\, th
 e importance of lateral transfers in evolution has been clearly recognized
  and increasing doubts are being raised about the feasibility of inferring
  a tree of life based on genome evolution. Identifying lateral gene transf
 ers is therefore an important problem in evolutionary biology. A distance 
 matrix in the Farris space that fulfills the Kalmanson inequalities can be
  exactly represented by an outer-planar split network or an X-tree. Using 
 a simple model of lateral transfers one can show that a circular order of 
 the taxa labeling the leaves of a phylogenetic tree is quite robust to lat
 eral transfers. A circular order of the taxa is an order in which the taxa
  are encountered through a clockwise scanning of the tree embedded in the 
 plane. If lateral transfers occur only between consecutive taxa in such an
  order\, it can be shown that\, within the usual distance-based framework 
 for constructing phylogenetic trees\, the o uter-planar network reconstruc
 ted by applying the Neighbor-Net algorithm furnishes a circular order of t
 he nodes with the corresponding distance matrices in the Farris space fulf
 illing the Kalmanson inequalities. There are limits to the robustness of a
  circular order to lateral transfers. A new approach has been developed to
  deal with such cases. The approach combines the Neighbor-Net algorithm fo
 r 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 cri
 terion is then used to identify the taxa possibly involved in a lateral tr
 ansfer between non-consecutive taxa. We illustrate the utility of the new 
 approach by applying it to a distance matrix for Archaea\, Bacteria and Eu
 karyota. Our new approach gives a way to use Neighbor-Nets to get further 
 biological information\, for instance to localize and identify lateral tra
 nsfers or to detect large deviations to an X-tree or an outer-planar split
  network topology using distance matrices. In future\, we expect that it w
 ill become increasingly important to develop new tools such as this that c
 an allow one to extract useful biological information from trees and netwo
 rks. \n
LOCATION:Seminar Room 1\, Newton Institute
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