A multi-stack based phylogenetic tree building method

  • Authors:

  • Róbert Busa-Fekete;András Kocsor;Csaba Bagyinka

  • Affiliations:

  • Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Hungary;Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Hungary;Institute of Biophysics, Biological Research Center of the Hung. Acad. Sci, Hungary

  • Venue:
  • ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
  • Year:
  • 2007

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Abstract

Here we introduce a new Multi-Stack (MS) based phylogenetic tree building method. The Multi-Stack approach organizes the candidate subtrees (i.e. those having same number of leaves) into limited priority queues, always selecting the K-best subtrees, according to their distance estimation error. Using the K-best subtrees our method iteratively applies a novel subtree joining strategy to generate candidate higher level subtrees from the existing low-level ones. This new MS method uses the Constrained Least Squares Criteria (CLSC) which guarantees the non-negativity of the edge weights. The method was evaluated on real-life datasets as well as on artificial data. Our empirical study consists of three very different biological domains, and the artificial tests were carried out by applying a proper model population generator which evolves the sequences according to the predetermined branching pattern of a randomly generated model tree. The MS method was compared with the Unweighted Pair Group Method (UPGMA), Neighbor-Joining (NJ), Maximum Likelihood (ML) and Fitch-Margoliash (FM) methods in terms of Branch Score Distance (BSD) and Distance Estimation Error (DEE). The results show clearly that the MS method can achieve improvements in building phylogenetic trees.