Generalized neighbor-joining: more reliable phylogenetic tree reconstruction

We have developed a phylogenetic tree reconstruction method that detects and reports multiple topologically distant low-cost solutions. Our method is a generalization of the neighbor-joining method of Saitou and Nei and affords a more thorough sampling of the solution space by keeping track of multiple partial solutions during its execution. The scope of the solution space sampling is controlled by a pair of user-specified parameters--the total number of alternate solutions and the number of alternate solutions that are randomly selected--effecting a smooth trade-off between run time and solution quality and diversity. This method can discover topologically distinct low-cost solutions. In tests on biological and synthetic data sets using either the least-squares distance or minimum-evolution criterion, the method consistently performed as well as, or better than, both the neighbor-joining heuristic and the PHYLIP implementation of the Fitch-Margoliash distance measure. In addition, the method identified alternative tree topologies with costs within 1% or 2% of the best, but with topological distances of 9 or more partitions from the best solution (16 taxa); with 32 taxa, topologies were obtained 17 (least-squares) and 22 (minimum-evolution) partitions from the best topology when 200 partial solutions were retained. Thus, the method can find lower-cost tree topologies and near-best tree topologies that are significantly different from the best topology.