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Détection de nouveaux domaines protéiques par co-occurence : Application à P. falciparum

Nicolas Terrapon 1 Olivier Gascuel 2, * Laurent Brehelin 2
* Corresponding author
2 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Hidden Markov Models (HMMs) have proved to be powerful for protein domain identification. However, numerous domains may be missed in highly divergent proteins. This is the case for the proteins of Plasmodium falciparum, the main causal agent of human malaria. Here, we propose a method that uses domain co-occurrence to increase the sensitivity of the approach while controlling its false discovery rate. Applied to P. falciparum, our method identify (with an error rate below 20%) 482 new domains (versus 3482 in PlasmoDB), which involve 158 new GO annotations.
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Contributor : Laurent Brehelin <>
Submitted on : Thursday, September 10, 2009 - 10:35:27 AM
Last modification on : Friday, February 12, 2021 - 5:24:02 PM
Long-term archiving on: : Tuesday, June 15, 2010 - 9:40:12 PM


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  • HAL Id : lirmm-00414954, version 1



Nicolas Terrapon, Olivier Gascuel, Laurent Brehelin. Détection de nouveaux domaines protéiques par co-occurence : Application à P. falciparum. JOBIM'09 : Journées Ouvertes en Biologie, Informatique et Mathématiques, Jun 2009, Nantes, France. pp.43-48. ⟨lirmm-00414954⟩



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