HIDDEN MARKOV-MODELS OF BIOLOGICAL PRIMARY SEQUENCE INFORMATION

Hidden Markov model (HMM) techniques are used to model families of bio logical sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to t hree protein families: globins, immunoglobulins, and kinases. In all c ases, the models derived capture the important statistical characteris tics of the family and can be used for a number of tasks, including mu ltiple alignments, motif detection, and classification. For K sequence s of average length N, this approach yields an effective multiple-alig nment algorithm which requires O(KN2) operations, linear in the number of sequences.