Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments.
Any effective phylogeny inference based on molecular data begins by performing efficient multiple sequence alignments. So far, the Hidden Markov Model (HMM) method for multiple sequence alignment has been proved competitive to the classical deterministic algorithms with respect to phylogenetic analysis; nevertheless, its stochastic nature does not help it cope with the existing dependence among the sequence elements. This paper deals with phylogenetic analysis of protein and gene data using multiple sequence alignments produced by fuzzy profile Hidden Markov Models. Fuzzy profile HMMs are a novel type of profile HMMs based on fuzzy sets and fuzzy integrals, which generalize the classical stochastic HMM by relaxing its independence assumptions. In this paper, alignments produced by the fuzzy HMM model are used in phylogenetic analysis of protein data, enhancing the quality of phylogenetic trees. The new methodology is implemented in HPV virus phylogenetic inference. The results of the analysis are compared against those obtained by the classical profile HMM model and depict the superiority of the fuzzy profile HMM in this field.
Collyda
Chrysa
C
Lab of Medical Informatics, Faculty of Medicine, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece.
Diplaris
Sotiris
S
Mitkas
Pericles
P
Maglaveras
Nicos
N
Pappas
Costas
C
D019295:Computational Biology; D017143:Fuzzy Logic; D008390:Markov Chains; D010802:Phylogeny; D016415:Sequence Alignment