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Paper   IPM / Biological Sciences / 14428
School of Biological Sciences
  Title:   Generalized Profile Hidden Markov Model (PHMM) based on the Dependency between sequences
1.  Vahid Rezaei Tabar
2.  Hamid Pezeshk
  Status:   Published
  Journal: Progress in Biological Sciences
  No.:  2
  Vol.:  6
  Year:  2016
  Pages:   117-127 10.22059/PBS.2016.590014
  Supported by:  IPM
The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on their similarity; the one-by-one dependency between corresponding amino acids of two current sequences can be append to PHMM. This perspective makes it possible to consider a generalization of PHMM. For estimating the parameters of modified PHMM (emission and transition probabilities), we introduce new forward and backward algorithms. For this purpose, we consider the generalized PHMM as a Bayesian Network (BN). A Bayesian network is a specific type of graphical model which is a directed acyclic graph (DAG). The performance of modified PHMM is discussed by applying it to the twenty protein families in Pfam database. Results show that the modified PHMM significantly increases the accuracy of ordinary PHMM.

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