title: LEON-BIS: multiple alignment evaluation of sequence neighbours using a Bayesian inference system.
abstract: BACKGROUND
  A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, protein functional annotation, prediction of molecular interactions, etc. These applications, however sophisticated, are generally highly sensitive to the alignment used, and neglecting non-homologous or uncertain regions in the alignment can lead to significant bias in the subsequent inferences.
  
  
  RESULTS
  Here, we present a new method, LEON-BIS, which uses a robust Bayesian framework to estimate the homologous relations between sequences in a protein multiple alignment. Sequences are clustered into sub-families and relations are predicted at different levels, including 'core blocks', 'regions' and full-length proteins. The accuracy and reliability of the predictions are demonstrated in large-scale comparisons using well annotated alignment databases, where the homologous sequence segments are detected with very high sensitivity and specificity.
  
  
  CONCLUSIONS
  LEON-BIS uses robust Bayesian statistics to distinguish the portions of multiple sequence alignments that are conserved either across the whole family or within subfamilies. LEON-BIS should thus be useful for automatic, high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc.
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.
authors.forename: Renaud
authors.initials: R
authors.lastname: Vanhoutreve
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.
authors.forename: Arnaud
authors.initials: A
authors.lastname: Kress
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.
authors.forename: Baptiste
authors.initials: B
authors.lastname: Legrand
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.
authors.forename: Hélène
authors.initials: H
authors.lastname: Gass
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Poch
authors.affiliation: Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France. thompson@unistra.fr.
authors.forename: Julie D
authors.initials: JD
authors.lastname: Thompson
mesh_terms: D000595:Amino Acid Sequence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins
keywords: Bayesian statistics; Homology-based methods; Multiple sequence alignment; Sequence homology