Two New Distance Based Methods For Phylogenetic Tree Reconstruction

Phylogenetic tree reconstruction is one of the most important felds of bioin-formatics. Using phylogenetic tree, we can study the relationship between speciesand classify the new discovered species. In the construction of phylogenetic trees,many algorithms have been proposed that can be generally divided into threecategories: distance based methods, maximum-parsimony methods, and the max-imum likelihood methods.In this paper, we propose two new methods for phylogenetic tree reconstruc-tion. One is called double selecting (DS) method. Gronau and Moran proposeda Deepest Least Common Ancestor (DLCA) method for phylogenetic tree recon-struction. Our another method is a modifed version of DLCA and called MDLCAmethod. Using some data sets, we study the accuracy of these two methods inobtaining the correct unrooted tree in comparison with that of three other tree-making methods: the neighborjoining (NJ) method, the unweighted pair group(UPGMA) method and DLCA algorithms. The results show that the DS methodand mDLCA method perform as well as the NJ method, and are better thanUPGMA and DLCA methods. Especially, the DS method even is slightly betterthan the NJ method in some cases. In addition, we also study the diference ofthese two methods in the phylogenetic tree reconstruction.