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

Gregory Kucherov, Laurent Noé, Mikhail Roytberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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 unite 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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages251-263
Number of pages13
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event5th International Workshop on Algorithms in Bioinformatics, WABI 2005 - Mallorca, Spain
Duration: 3 Oct 20056 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3692 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Algorithms in Bioinformatics, WABI 2005
Country/TerritorySpain
CityMallorca
Period3/10/056/10/05

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