title: Probabilistic annotation of protein sequences based on functional classifications.
abstract: BACKGROUND
  One of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics.
  
  
  RESULTS
  Here, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases.
  
  
  CONCLUSIONS
  The performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines.
authors.affiliation: Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK. elevy@mrc-lmb.cam.ac.uk
authors.forename: Emmanuel D
authors.initials: ED
authors.lastname: Levy
authors.forename: Christos A
authors.initials: CA
authors.lastname: Ouzounis
authors.forename: Walter R
authors.initials: WR
authors.lastname: Gilks
authors.forename: Benjamin
authors.initials: B
authors.lastname: Audit
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D017124:Conserved Sequence; D004282:Documentation / Q000379:methods; D004798:Enzymes / Q000737:chemistry* / Q000378:metabolism*; D019143:Evolution, Molecular; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D004798:Enzymes