Abstract
Profile Hidden Markov Models (PHMMs) are recognized as powerful computational vehicles for homology search of protein sequences. Extant PHMM training approaches either use completely unaligned or aligned sequences. The PHMMs resulting from these two training approaches present contrasting tradeoffs w.r.t. alignment information and the accuracy of the search outcome. This paper describes a PHMM based technique for modeling protein families from partially aligned sequences. By exploiting the observation that partially aligned sequences give rise to independent subsequences, PHMMs corresponding to these subsequences are composed to build PHMMs for the entire sequences. An interesting aspect of the technique is that it gives rise to a family of PHMMs which are parameterized w.r.t. the alignment information. We present experimental comparison of the performance of our technique against several state of the art homology detection methods.
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