title: High similarity sequence comparison in clustering large sequence databases.
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.
authors.affiliation: IMT, INRIA, Marseille Cedex 20, 13451, France. Lorie.Dudoignon@sophia.inria.fr
authors.forename: Lorie
authors.initials: L
authors.lastname: Dudoignon
authors.forename: Eric
authors.initials: E
authors.lastname: Glemet
authors.forename: Hendrik Cornelis
authors.initials: HC
authors.lastname: Heus
authors.forename: Mathieu
authors.initials: M
authors.lastname: Raffinot
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003628:Database Management Systems; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*