Portal de Periódicos da CAPES

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity

2015; Oxford University Press; Volume: 43; Issue: W1 Linguagem: Inglês

10.1093/nar/gkv523

1362-4962

Damiano Piovesan, Manuel Giollo, Emanuela Leonardi, Carlo Ferrari, Silvio C. E. Tosatto,

Machine Learning in Bioinformatics

Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.