Title |
Quality of Computationally Inferred Gene Ontology Annotations
|
---|---|
Published in |
PLoS Computational Biology, May 2012
|
DOI | 10.1371/journal.pcbi.1002533 |
Pubmed ID | |
Authors |
Nives Škunca, Adrian Altenhoff, Christophe Dessimoz |
Abstract |
Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon-an important outcome given that >98% of all annotations are inferred without direct curation. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 28% |
United Kingdom | 2 | 6% |
Colombia | 2 | 6% |
Switzerland | 2 | 6% |
Denmark | 2 | 6% |
Germany | 1 | 3% |
Sweden | 1 | 3% |
Chile | 1 | 3% |
Netherlands | 1 | 3% |
Other | 4 | 13% |
Unknown | 7 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 19 | 59% |
Members of the public | 12 | 38% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | 5% |
United Kingdom | 7 | 3% |
Canada | 3 | 1% |
Spain | 3 | 1% |
Netherlands | 2 | <1% |
Italy | 2 | <1% |
Brazil | 2 | <1% |
Sweden | 1 | <1% |
Portugal | 1 | <1% |
Other | 5 | 2% |
Unknown | 166 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 52 | 26% |
Student > Ph. D. Student | 51 | 25% |
Student > Bachelor | 20 | 10% |
Student > Master | 19 | 9% |
Professor | 11 | 5% |
Other | 34 | 17% |
Unknown | 16 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 109 | 54% |
Computer Science | 28 | 14% |
Biochemistry, Genetics and Molecular Biology | 24 | 12% |
Engineering | 3 | 1% |
Medicine and Dentistry | 3 | 1% |
Other | 17 | 8% |
Unknown | 19 | 9% |