High Similarity Sequence Comparison in Clustering Large Sequence Databases

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	Similar Articles Articles by Lorie Dudoignon Articles by Eric Glemet Articles by Hendrik Cornelis Heus Articles by Mathieu Raffinot

IEEE Computer Society Bioinformatics Conference (CSB'02)

Stanford, California

August 14-August 16

ISBN: 0-7695-1653-X

	ASCII Text	x
	Lorie Dudoignon, Eric Glemet, Hendrik Cornelis Heus, Mathieu Raffinot, "High Similarity Sequence Comparison in Clustering Large Sequence Databases," Computational Systems Bioinformatics Conference, International IEEE Computer Society, pp. 228, IEEE Computer Society Bioinformatics Conference (CSB'02), 2002.

	BibTex	x
	@article{ 10.1109/CSB.2002.1039345, author = {Lorie Dudoignon and Eric Glemet and Hendrik Cornelis Heus and Mathieu Raffinot}, title = {High Similarity Sequence Comparison in Clustering Large Sequence Databases}, journal ={Computational Systems Bioinformatics Conference, International IEEE Computer Society}, volume = {0}, year = {2002}, isbn = {0-7695-1653-X}, pages = {228}, doi = {http://doi.ieeecomputersociety.org/10.1109/CSB.2002.1039345}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computational Systems Bioinformatics Conference, International IEEE Computer Society TI - High Similarity Sequence Comparison in Clustering Large Sequence Databases SN - 0-7695-1653-X SP EP A1 - Lorie Dudoignon, A1 - Eric Glemet, A1 - Hendrik Cornelis Heus, A1 - Mathieu Raffinot, PY - 2002 KW - null VL - 0 JA - Computational Systems Bioinformatics Conference, International IEEE Computer Society ER -

Lorie Dudoignon, IMT, INRIA

Eric Glemet, Gene-it

Hendrik Cornelis Heus, Gene-it

Mathieu Raffinot, CNRS

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSB.2002.1039345

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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