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Simsearcher: a local similarity search engine for biological sequence databases

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2 Author(s)
Tian-Haw Tsai ; Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Suh-Yin Lee

An efficient local similarity search engine is developed by exploiting some techniques of data mining. All frequent patterns in the database are retrieved and recorded in a one-time preprocessing process. Then a query sequence is checked to see whether any pattern from the preprocessing stage is matched to the query. Two regions coming from the query and a database sequence that both match a pattern form a possible seed for local similarity. Finally, we extend and score each such seed region pair to see whether there really exists local similarity with a score high enough for reporting. For computational efficiency, a novel clustering approach is proposed and integrated into the proposed system, which is based on the local similarity search engine - the DELPHI system proposed by IBM. Extensive experiments are demonstrated to show the performance of our system.

Published in:

Multimedia Software Engineering, 2003. Proceedings. Fifth International Symposium on

Date of Conference:

10-12 Dec. 2003