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Modelling Biological Sequences by Grammatical Inference

François Coste 1, * 
* Corresponding author
1 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Recent sequencing projects and technological progress in the field are giving access to an ever increasing amount of DNA, RNA and proteins macromolecular sequences. To annotate these sequences, fundamental and important tasks in Bioinformatics are the comparisons of sequences to determine common or consensus patterns among a family of sequences, discriminate members of the family from non-members, and discover new members of the family. In this tutorial, we propose a quick introduction to the world of the biological macromolecules and we will survey the approaches related to grammatical inference which have been developed in Bioinformatics to model these sequences, from well established weighting schemes for Profile HMM and Stochastic Context-Free grammars to approaches learning (also) the structure or topology of the grammars.
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Submitted on : Tuesday, November 16, 2010 - 10:26:32 AM
Last modification on : Friday, February 4, 2022 - 3:25:00 AM
Long-term archiving on: : Friday, October 26, 2012 - 3:41:32 PM


  • HAL Id : inria-00531552, version 1


François Coste. Modelling Biological Sequences by Grammatical Inference. ICGI 2010 Tutorial Day, José M. Sempere and Pedro Garcias, Sep 2010, Valencia, Spain. ⟨inria-00531552⟩



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