%0 Journal Article
%T INGA 2.0: improving protein function prediction for the dark proteome
%A Damiano Piovesan
%A Silvio C E Tosatto
%J Archive of "Nucleic Acids Research".
%D 2019
%R 10.1093/nar/gkz375
%X Our current knowledge of complex biological systems is stored in a computable form through the Gene Ontology (GO) which provides a comprehensive description of genes function. Prediction of GO terms from the sequence remains, however, a challenging task, which is particularly critical for novel genomes. Here we present INGA 2.0, a new version of the INGA software for protein function prediction. INGA exploits homology, domain architecture, interaction networks and information from the ¡®dark proteome¡¯, like transmembrane and intrinsically disordered regions, to generate a consensus prediction. INGA was ranked in the top ten methods on both CAFA2 and CAFA3 blind tests. The new algorithm can process entire genomes in a few hours or even less when additional input files are provided. The new interface provides a better user experience by integrating filters and widgets to explore the graph structure of the predicted terms. The INGA web server, databases and benchmarking are available from URL: https://inga.bio.unipd.it/
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602455/