Computational Methods for Annotation Transfers from Sequence.
Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.
Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
Domenico
D
Cozzetto
Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK. d.t.jones@ucl.ac.uk.
David T
DT
Jones
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D063990:Gene Ontology; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D010802:Phylogeny; D011506:Proteins / Q000235:genetics / Q000378:metabolism
D011506:Proteins
De novo function prediction; Homology-based annotation transfers; Multi-domain architecture; Phylogenomics; Protein function prediction