High similarity sequence comparison in clustering large sequence databases.

Lorie Dudoignon; Eric Glemet; Hendrik Cornelis Heus; Mathieu Raffinot

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PubMed · 15838139

High similarity sequence comparison in clustering large sequence databases.

Lorie Dudoignon·Eric Glemet·Hendrik Cornelis Heus·Mathieu Raffinot

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Abstract

We present a fast algorithm for sequence clustering and searching which works with large sequence databases. 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.

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Algorithms Chromosome Mapping Cluster Analysis Database Management Systems Databases, Genetic Information Storage and Retrieval Pattern Recognition, Automated Sequence Alignment Sequence Analysis Comparative Study Evaluation Study Journal Article

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Lorie Dudoignon, Eric Glemet, Hendrik Cornelis Heus, Mathieu Raffinot. 2002. High similarity sequence comparison in clustering large sequence databases.. https://pubmed.ncbi.nlm.nih.gov/15838139/

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