Sequence Homology Search Based on Database Indexing Using the Profile Hidden Markov Model

The profile hidden Markov model (PHMM) has received increasing attention in the field of protein homology detection, since profile-based methods are much more sensitive in detecting distant homologous relationships than pairwise methods. Pure dynamic-programming-based systems are often used for PHMM searches. However, these dynamic-programming- based systems are very time consuming for a large database. For instance, it may take approximately 15 minutes to search a short model of length 12 in the GenBank protein sequence database. Instead of searching the database sequentially, we search the database based on a tree-structured database indexing, called the HD-tree. The HD-tree is able to reduce the PHMM search time significantly without reducing the quality of search results. Performance of search using the HD-tree is compared with that of HMMER, a popular implementation of PHMM for protein sequence analysis. It is shown that the HD-tree approach is orders of magnitude faster than HMMER for short queries