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KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1

Damien Eveillard 1, 2 Abdelhalim Larhlimi 1 Delphine Ropers 3 Stéphanie Billaut 1 Sandrine Peyrefitte 1, 2
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 HELIX - Computer science and genomics
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
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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Contributor : Damien Eveillard Connect in order to contact the contributor
Submitted on : Thursday, December 8, 2005 - 5:58:27 PM
Last modification on : Friday, February 4, 2022 - 3:33:34 AM
Long-term archiving on: : Friday, April 2, 2010 - 11:00:37 PM


  • HAL Id : inria-00000915, version 1


Damien Eveillard, Abdelhalim Larhlimi, Delphine Ropers, Stéphanie Billaut, Sandrine Peyrefitte. KOALAB: A new method for regulatory motif search. Illustration on alternative splicing regulation in HIV-1. 5èmes Journées Ouvertes Biologie Informatique Mathématiques - JOBIM 2004, Jun 2004, Montréal, Canada. ⟨inria-00000915⟩



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