Random local neighbor joining: A new method for reconstructing phylogenetic trees

Abstract

We have developed a new method for reconstructing phylogenetic trees called random local neighbor-joining (RLNJ). Our method is different from the neighbor-joining method (NJ) of Saitou and Nei and affords a more thorough sampling of solution space by randomly searching for local pair of neighbors in each step. Results using the RLNJ method to analyze yeast data show an increasing possibility to get a smaller S value (sum of branch lengths) compared with the NJ method as cases with more taxa are analyzed and many individual runs using the RLNJ method usually generate more than one topology with small S values. Computer simulation shows the fact that the RLNJ method can improve the possibility of recovering correct topology significantly by affording more than one topology. In addition, when using the RLNJ method, computer simulation also shows that the proportion of correct topologies ( P C) will increase as the number of different topologies decreases and as the proportion of “most frequent topology” increases. Thus, the number of different topologies and the proportion of “most frequent topology” can be used as auxiliary criteria to evaluate reliability of a phylogenetic tree.