KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1
Identifieur interne : 002D41 ( Hal/Corpus ); précédent : 002D40; suivant : 002D42KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1
Auteurs : Damien Eveillard ; Abdelhalim Larhlimi ; Delphine Ropers ; Stéphanie Billaut ; Sandrine PeyrefitteSource :
English descriptors
Abstract
Discovering heterogeneous regulatory motifs remains a difficult problem in biological sequence analysis. In this context, statistical learning or pattern search techniques on their own have shown some limitations. However, significant benefits can be taken from their complementarity. We selected two state-of-the-art methods: a multi-class support vector machine (M-SVM) from the statistical learning domain associated with a performant discrete pattern matching algorithm grappe, and in- tegrated them into a web technology based graphical software: KOALAB (KOupled Algorithmic and Learning Approach for Biology)1 . We applied our method on motif discovery within nucleic acid sequences using experimental SELEX results as training database for the M-SVM. An application dealing with the search for splicing regulatory protein binding sites in HIV-1 genome shows the potential of such an approach.
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Hal:inria-00000915Le document en format XML
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<front><div type="abstract" xml:lang="en">Discovering heterogeneous regulatory motifs remains a difficult problem in biological sequence analysis. In this context, statistical learning or pattern search techniques on their own have shown some limitations. However, significant benefits can be taken from their complementarity. We selected two state-of-the-art methods: a multi-class support vector machine (M-SVM) from the statistical learning domain associated with a performant discrete pattern matching algorithm grappe, and in- tegrated them into a web technology based graphical software: KOALAB (KOupled Algorithmic and Learning Approach for Biology)1 . We applied our method on motif discovery within nucleic acid sequences using experimental SELEX results as training database for the M-SVM. An application dealing with the search for splicing regulatory protein binding sites in HIV-1 genome shows the potential of such an approach.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1</title>
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<author role="aut"><persName><forename type="first">Delphine</forename>
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<author role="aut"><persName><forename type="first">Sandrine</forename>
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<idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
<idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno>
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<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
<idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="BIOENVIS">Fédération de Recherche BioEnviS, BioEnvironnement et Santé (Université de Lyon)</idno>
<idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno>
<idno type="stamp" n="UNIV-NANTES">Université de Nantes</idno>
<idno type="stamp" n="LINA-COMBI" p="LINA">LINA - Equipe COMbinatoire et Bio-Informatique</idno>
<idno type="stamp" n="LINA" p="UNIV-NANTES">Laboratoire d'Informatique de Nantes Atlantique</idno>
<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1</title>
<author role="aut"><persName><forename type="first">Damien</forename>
<surname>Eveillard</surname>
</persName>
<email>damien.eveillard@univ-nantes.fr</email>
<idno type="idHal">damien-eveillard</idno>
<idno type="halAuthorId">73614</idno>
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<author role="aut"><persName><forename type="first">Abdelhalim</forename>
<surname>Larhlimi</surname>
</persName>
<idno type="halAuthorId">73615</idno>
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</author>
<author role="aut"><persName><forename type="first">Delphine</forename>
<surname>Ropers</surname>
</persName>
<email>delphine.ropers@inria.fr</email>
<idno type="idHal">delphine-ropers</idno>
<idno type="halAuthorId">73616</idno>
<affiliation ref="#struct-2378"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Stéphanie</forename>
<surname>Billaut</surname>
</persName>
<idno type="halAuthorId">73617</idno>
<affiliation ref="#struct-2356"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Sandrine</forename>
<surname>Peyrefitte</surname>
</persName>
<idno type="halAuthorId">73618</idno>
<affiliation ref="#struct-2356"></affiliation>
<affiliation ref="#struct-1112"></affiliation>
</author>
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<monogr><meeting><title>5èmes Journées Ouvertes Biologie Informatique Mathématiques - JOBIM 2004</title>
<date type="start">2004-06</date>
<settlement>Montréal, Canada</settlement>
</meeting>
<imprint><date type="datePub">2004-06</date>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><keywords scheme="author"><term xml:lang="en">motif discovery</term>
<term xml:lang="en">multi-class SVM</term>
<term xml:lang="en">SELEX</term>
<term xml:lang="en">alternative splicing regulation</term>
</keywords>
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<classCode scheme="halDomain" n="sdv.bbm.gtp">Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]</classCode>
<classCode scheme="halDomain" n="sdv.bibs">Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]</classCode>
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<abstract xml:lang="en">Discovering heterogeneous regulatory motifs remains a difficult problem in biological sequence analysis. In this context, statistical learning or pattern search techniques on their own have shown some limitations. However, significant benefits can be taken from their complementarity. We selected two state-of-the-art methods: a multi-class support vector machine (M-SVM) from the statistical learning domain associated with a performant discrete pattern matching algorithm grappe, and in- tegrated them into a web technology based graphical software: KOALAB (KOupled Algorithmic and Learning Approach for Biology)1 . We applied our method on motif discovery within nucleic acid sequences using experimental SELEX results as training database for the M-SVM. An application dealing with the search for splicing regulatory protein binding sites in HIV-1 genome shows the potential of such an approach.</abstract>
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