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Probabilistic annotation of protein sequences based on functional classifications |
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| Authors: | Emmanuel?D?Levy,Christos?A?Ouzounis mailto:ouzounis@ebi.ac.uk" title=" ouzounis@ebi.ac.uk" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author,Walter?R?Gilks,Benjamin?Audit mailto:Benjamin.Audit@ens-lyon.fr" title=" Benjamin.Audit@ens-lyon.fr" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author |
| Affiliation: | (1) Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, CB10 1SD Cambridge, UK;(2) Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 2SR, UK;(3) Computational Genomics Group, MRC Laboratory of Molecular Biology, Hills Rd, Cambridge, CB2 2QH, UK;(4) Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Suprieure, 46 Alle d'Italie, 69364 Lyon Cedex 07, France |
| Abstract: | BackgroundOne 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. |
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