Efficient genome-scale phylogenetic analysis under the duplication-loss and deep coalescence cost models

Background: Genomic data provide a wealth of new information for phylogenetic analysis. Yet making use of this data requires phylogenetic methods that can efficiently analyze extremely large data sets and account for processes of gene evolution, such as gene duplication and loss, incomplete lineage sorting (deep coalescence), or horizontal gene transfer, that cause incongruence among gene trees. One such approach is gene tree parsimony, which, given a set of gene trees, seeks a species tree that requires the smallest number of evolutionary events to explain the incongruence of the gene trees. However, the only existing algorithms for gene tree parsimony under the duplication-loss or deep coalescence reconciliation cost are prohibitively slow for large datasets. Results: We describe novel algorithms for SPR and TBR based local search heuristics under the duplication-loss cost, and we show how they can be adapted for the deep coalescence cost. These algorithms improve upon the best exis...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Mukul S. Bansal, J. Gordon Burleigh, Oliver Eulenstein
• Keywords: BMCBI 2010, Deep Coalescence, Gene Tree, Gene Tree Parsimony