2005/12/14 by Emmanuel D. Levy, Christos Ouzounis, Christos A. Ouzounis +2 · 30 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · #Advanced Proteomics Techniques and Applications #Annotation #Artificial intelligence #Bayesian probability #Bioinformatics and Genomic Networks #Biology #Cluster analysis #Computational biology #Computer science #Data mining #Function (biology) #Genetics #Machine Learning in Bioinformatics #Probabilistic logic #Protein function #Protein function prediction #Sequence (biology) #Similarity (geometry) #q-bio.QM
paper · pdf · doi:10.1186/1471-2105-6-302
published in BMC Bioinformatics 6(1), 302 (BioMed Central)
openalex publication_date 2005/12/14 · arxiv created 2007/09/27 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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. CONCLUSION: 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.