Title :

A unifying framework for seed sensitivity and its application to subset seeds.

Author(s) :

Kucherov, Gregory [Auteur]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Noé, Laurent [Auteur correspondant]
Sequential Learning [SEQUOIA]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Roytberg, Mihkail [Auteur]
Institute of Mathematical Problems in Biology [IMPB RAS]

Journal title :

Journal of Bioinformatics and Computational Biology

Pages :

553-69

Publisher :

World Scientific Publishing

Publication date :

2006-04

ISSN :

0219-7200

HAL domain(s) :

Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]

English abstract : [en]

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem--a set of target alignments, an ...
Show more >We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem--a set of target alignments, an associated probability distribution, and a seed model--that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL