Data Mining Using Hidden Markov Models (HMM2) to Detect Heterogeneities into Bacteria Genomes - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2005

Data Mining Using Hidden Markov Models (HMM2) to Detect Heterogeneities into Bacteria Genomes

Abstract

The Streptococcus genus contains both pathogenic bacteria and bacteria used in the food-processing industry. We are developing a statistical segmentation method to identify heterogeneous sequences such as sequences acquired from recent horizontal transfer or genes weakly or strongly expressed. The method is based on second order Hidden Markov Models (HMM2). After an automatic unsupervised training, this method allows to demarcating some particular areas into a genome. After checking the efficiency of such models on various controls and on chimeric sequences generated in silico, we choose a HMM2 (3-mer, 5 states) to analyse the complete genome sequence of S. Thermophilus CNRZ1066 (1.8 Mb). More the 80 atypical segments were extracted and are currently analysed further.
Fichier principal
Vignette du fichier
PosterEng_JOBIM2005.pdf (159.68 Ko) Télécharger le fichier
Loading...

Dates and versions

inria-00000142 , version 1 (03-07-2005)

Identifiers

  • HAL Id : inria-00000142 , version 1

Cite

Catherine Eng, Annabelle Thibessard, Sébastien Hergalant, Jean-François Mari, Pierre Leblond. Data Mining Using Hidden Markov Models (HMM2) to Detect Heterogeneities into Bacteria Genomes. Journées Ouvertes Biologie, Informatique et Mathématiques - JOBIM 2005, JOBIM, Jul 2005, Lyon/France, France. ⟨inria-00000142⟩
256 View
23638 Download

Share

Gmail Facebook Twitter LinkedIn More