A rapid heuristic algorithm for finding minimum evolution trees

Prospects for inferring very large phylogenies by using the neighbor-joining method

Current efforts to reconstruct the tree of life and histories of multigene families demand the inference of phylogenies consisting of thousands of gene sequences. However, for such large data sets even a moderate exploration of the tree space needed to identify the optimal tree is virtually impossible. For these cases the neighbor-joining (NJ) method is frequently used because of its demonstrated accuracy for smaller data sets and its computational speed. As data sets grow, however, the fraction of the tree space examined by the NJ algorithm becomes minuscule. Here, we report the results of our computer simulation for examining the accuracy of NJ trees for inferring very large phylogenies. First we present a likelihood method for the simultaneous estimation of all pairwise distances by using biologically realistic models of nucleotide substitution. Use of this method corrects up to 60% of NJ tree errors. Our simulation results show that the accuracy of NJ trees decline only by approximately 5% when the number of sequences used increases from 32 to 4,096 (128 times) even in the presence of extensive variation in the evolutionary rate among lineages or significant biases in the nucleotide composition and transition/transversion ratio. Our results encourage the use of complex models of nucleotide substitution for estimating evolutionary distances and hint at bright prospects for the application of the NJ and related methods in inferring large phylogenies.

NJML+: an extension of the NJML method to handle protein sequence data and computer software implementation

While the maximum-likelihood (ML) method of tree reconstruction is statistically rigorous, it is extremely time-consuming for reconstructing large trees. We previously developed a hybrid method (NJML) that combines the neighbor-joining (NJ) and ML methods and thus is much faster than the ML method and improves the performance of NJ. However, we considered only nucleotide sequence data, so NJML is not suitable for handling amino acid sequence data, which requires even more computer time. NJML+ is an implementation of a further improved method for practical data analyses (including protein sequence data). Our extensive simulations using nucleotide and amino acid sequences showed that NJML+ gave good results in tree reconstruction. Indeed, NJML+ showed substantial improvements over existing methods in terms of both computational times and efficiencies, especially for amino acid sequence data. We also developed a "user-friendly" interface for the NJML+ program, including a simple tree viewer.