InforLorV4, Pmc, Corpus, bibRecord, 000073

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

Identifieur interne : 000073 ( Pmc/Corpus ); précédent : 000072; suivant : 000074

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

Auteurs : Gregory Kucherov ; Laurent Noé ; Mihkail Roytberg

Source :

Journal of Bioinformatics and Computational Biology [ 0219-7200 ] ; 2006.

RBID : PMC:2824148

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 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.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2824148

DOI: 10.1142/S0219720006001977
PubMed: 16819802
PubMed Central: 2824148

Links to Exploration step

PMC:2824148

Le document en format XML

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<affiliation><nlm:aff id="A1">LORIA<institution>INRIA</institution>
<institution>Université Henri Poincaré</institution>
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<author><name sortKey="Noe, Laurent" sort="Noe, Laurent" uniqKey="Noe L" first="Laurent" last="Noé">Laurent Noé</name>
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<author><name sortKey="Roytberg, Mihkail" sort="Roytberg, Mihkail" uniqKey="Roytberg M" first="Mihkail" last="Roytberg">Mihkail Roytberg</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A unifying framework for seed sensitivity and its application to subset seeds</title>
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<front><div type="abstract" xml:lang="en"><p id="P1">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 <italic>subset seeds</italic>
 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.</p>
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  <front><journal-meta><journal-id journal-id-type="nlm-ta">J Bioinform Comput Biol</journal-id>
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<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>A unifying framework for seed sensitivity and its application to subset seeds</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Kucherov</surname>
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<contrib contrib-type="author"><name><surname>Noé</surname>
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<xref ref-type="aff" rid="A1">1</xref>
<xref rid="FN1" ref-type="author-notes">*</xref>
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<author-notes><corresp id="FN1">* Correspondence should be adressed to: Laurent Noé <email>laurent.noe@lifl.fr</email>
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<fn id="FN2" fn-type="other"><label>#</label>
<p>part of this work has been done during a visit to LORIA/INRIA in summer 2004</p>
</fn>
</author-notes>
<pub-date pub-type="ppub"><month>4</month>
<year>2006</year>
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<pub-date pub-type="pmc-release"><day>18</day>
<month>2</month>
<year>2010</year>
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<volume>4</volume>
<issue>2</issue>
<fpage>553</fpage>
<lpage>569</lpage>
<abstract><p id="P1">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 <italic>subset seeds</italic>
 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.</p>
</abstract>
<kwd-group kwd-group-type="MESH"><kwd>Algorithms</kwd>
<kwd>Artificial Intelligence</kwd>
<kwd>Base Sequence</kwd>
<kwd>Molecular Sequence Data</kwd>
<kwd>Pattern Recognition, Automated</kwd>
<kwd>methods</kwd>
<kwd>Reproducibility of Results</kwd>
<kwd>Sensitivity and Specificity</kwd>
<kwd>Sequence Alignment</kwd>
<kwd>methods</kwd>
<kwd>Sequence Analysis, DNA</kwd>
<kwd>methods</kwd>
</kwd-group>
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A unifying framework for seed sensitivity and its application to subset seeds

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

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