Abstract
Approximate searching on the primary structure (i.e., amino acid arrangement) of protein sequences is an essential part in predicting the functions and evolutionary histories of proteins. However, because proteins distant in an evolutionary history do not conserve amino acid residue arrangements, approximate searching on proteins’ secondary structure is quite important in finding out distant homology. In this paper, we propose an indexing scheme for efficient approximate searching on the secondary structure of protein sequences which can be easily implemented in RDBMS. Exploiting the concept of clustering and lookahead, the proposed indexing scheme processes three types of secondary structure queries (i.e., exact match, range match, and wildcard match) very quickly. To evaluate the performance of the proposed method, we conducted extensive experiments using a set of actual protein sequences. According to the experimental results, the proposed method was proved to be faster than the existing indexing methods up to 6.3 times in exact match, 3.3 times in range match, and 1.5 times in wildcard match, respectively.
This work was supported by the Korea Research Foundation Grant. (KRF-2004-003-D00302)
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Seo, M., Park, S., Won, JI. (2005). CSI: Clustered Segment Indexing for Efficient Approximate Searching on the Secondary Structure of Protein Sequences. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_25
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DOI: https://doi.org/10.1007/11425274_25
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