Abstract
Neighbour-joining algorithm (NJ for short) is an used widely algorithm for constructing phylogenetic trees from distances because of its high accuracy. For NJ costs a lot of time to construct phylogenetic trees for the large input data, it does not often output a result within feasible time. Until now, there are several improved algorithms of NJ aiming for speeding up the construction of trees, but there is no research on the accuracy of those improved algorithms. The paper will analyze and compare the accuracy of NJ as well as its improved algorithms by means of the experiments. We introduce a new improved algorithm, called RandomNJ, which is an efficient method for constructing phylogenetic trees from distances. Furthermore, we design the INJ which is a web-based server for on-line constructing the phylogenetic trees using the improved algorithms and NJ. It is available from http://bioinformatics.imu.edu.cn/INJ/.
Highlights
NJ is initially proposed by Saitou and Nei [19] which can construct a phylogenetic tree
Reference [10] first uses NJ method to construct a phylogenetic tree for a protein set and uses the phylogenetic distances between proteins to predict the protein function
A connecting a and b, connect A with O, and delete the connection between nodes a, b and O; 7: update the distance matrix D by the formula 3; 8: n = n − 1; 9: end while 10: return T ; NJ and its improved algorithms take a distance matrix Dn×n as input, and output a phylogenetic tree, where n is the number of taxa
Summary
NJ is initially proposed by Saitou and Nei [19] which can construct a phylogenetic tree. Trees, the improved algorithms try to change the search strategy of the minimum value in the iteration. There are several improved methods that can not construct a right tree even for additive distance matrices. All improved methods can be divided into two classes based on the constructed trees for the additive distance matrices. FastJoin searches two minimum values in each iteration aiming to reduce the running time of NJ [24]. The other class is the algorithms which do not have any theoretical evidence and do not construct right trees for the additive distance matrices. We research on the accuracy of several typically improved algorithms, including NJ, FastJoin, RapidNJ, Clearcut, when they construct phylogenetic trees. We design a tool for researchers to conveniently construct phylogenetic trees using the NJ and its improved algorithms
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