H. Tanaka, K. Asai, M. Ishikawa, and A. Konagaya
There are many shared attributes between existing iterative aligners and Hidden Markov Model (HMM). A learning algorithm of HMM called Viterbi is the same as the iteration of DP-matching of iterative aligners. HMM aligners can use the result of an iterative aligner initially, incorporate the similarity score of amino acids, and apply the detailed gap cost systenm to improve the matching accuracy. On the other hand, the iterative aligner can inherit the modeling capability of HMM, and provide the better representation of the proteins than motifs. In this paper, we present an overview of several iterative ahgners which include the parallel iterative aligner of ICOT and the HMM aligner of Haussler’s group. We compare the merits and shortcomings of these aligners. This comparison enables us to formulate a better, more advanced aligner through proper integration of the iterative technique and HMM technique.