Abstract
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.
Highlights
With the exponential growth of biological sequence data, there has been an increasing interest in classifying records that share similarities in sequences, functions and structures together into families [1]
Detection of remote homology is more sensitive when a sequence query is searched against a family database than a PLOS ONE | DOI:10.1371/journal.pone
It is important to underscore that the obtained parameters are specific to TIGRFAM release 13, which was used for validating our approach
Summary
With the exponential growth of biological sequence data, there has been an increasing interest in classifying records that share similarities in sequences, functions and structures together into families [1]. Accelerating Information Retrieval form Profile Hidden Markov Model Databases sequence database [2]. Several approaches, such as regular expression, position weight matrix, and profiles, have been used to represent families of related biological sequences [3]. Profile Hidden Markov Model (profile-HMM) is one of the most sensitive approaches that has been used to represent similar biological strings as families [4]. The hallmark of profile-HMM is its ability to statistically represent the subtle conserved features of a given family of sequences
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