{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T01:37:35Z","timestamp":1768873055992,"version":"3.49.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T00:00:00Z","timestamp":1557446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Marie Sk\u0142odowska-Curie","award":["778247"],"award-info":[{"award-number":["778247"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 \u2018dark proteome\u2019, 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\/.<\/jats:p>","DOI":"10.1093\/nar\/gkz375","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T20:05:34Z","timestamp":1556654734000},"page":"W373-W378","source":"Crossref","is-referenced-by-count":27,"title":["INGA 2.0: improving protein function prediction for the dark proteome"],"prefix":"10.1093","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8210-2390","authenticated-orcid":false,"given":"Damiano","family":"Piovesan","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences, University of Padua, Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4525-7793","authenticated-orcid":false,"given":"Silvio C E","family":"Tosatto","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Padua, Padua, Italy"},{"name":"CNR Institute of Neuroscience, Padua, Italy"}]}],"member":"286","published-online":{"date-parts":[[2019,5,10]]},"reference":[{"key":"2019062808223340500_B1","doi-asserted-by":"crossref","first-page":"D330","DOI":"10.1093\/nar\/gky1055","article-title":"The Gene Ontology Resource: 20 years and still GOing strong","volume":"47","author":"The Gene Ontology Consortium","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2019062808223340500_B2","doi-asserted-by":"crossref","first-page":"D158","DOI":"10.1093\/nar\/gkw1099","article-title":"UniProt: the universal protein knowledgebase","volume":"45","author":"The UniProt Consortium","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"2019062808223340500_B3","doi-asserted-by":"crossref","first-page":"D1057","DOI":"10.1093\/nar\/gku1113","article-title":"The GOA database: gene Ontology annotation updates for 2015","volume":"43","author":"Huntley","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"2019062808223340500_B4","doi-asserted-by":"crossref","first-page":"D351","DOI":"10.1093\/nar\/gky1100","article-title":"InterPro in 2019: improving coverage, classification and access to protein sequence annotations","volume":"47","author":"Mitchell","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2019062808223340500_B5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nat. 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