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High Similarity Sequence Comparison in Clustering Large Sequence Databases
Stanford, California August 14-August 16
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSB.2002.1039345IEEE Computer Society Bioinformatics ...
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We present a fast algorithm for sequence clustering and searching which works with large sequence datab ases. It uses a strictly defined similarity measure. The algorithm is faster than conventional EST clustering approaches because its complexity is directly related to the number of subwords shared by the sequences. Furthermore, the algorithm also works with proteic sequences and large sequences like entire chromosomes. We present a theoretical study of our approach and provide experimental results.
Citation:
Lorie Dudoignon, Eric Glemet, Hendrik Cornelis Heus, Mathieu Raffinot, "High Similarity Sequence Comparison in Clustering Large Sequence Databases," csb, pp.228, IEEE Computer Society Bioinformatics Conference (CSB'02), 2002
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