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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining

Mao-Zu Guo, Jian-Fu Li* and Yang Liu

Author Affiliations

Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, P.R. China

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BMC Bioinformatics 2008, 9(Suppl 6):S4  doi:10.1186/1471-2105-9-S6-S4

Published: 28 May 2008

Abstract

Background

Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice.

Results

To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR.

Conclusion

Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time.