by 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 uponan important outcome given that >98\% of all annotations are inferred without direct curation.
Reference:
Quality of Computationally Inferred Gene Ontology Annotations (Nives Škunca, Adrian Altenhoff, Christophe Dessimoz), In PLoS Computational Biology (Lars Juhl Jensen, ed.), Public Library of Science, volume 8, 2012.
Bibtex Entry:
@article{Skunca2012,
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 uponan important outcome given that >98\% of all annotations are inferred without direct curation.},
author = {\v{S}kunca, Nives and Altenhoff, Adrian and Dessimoz, Christophe},
doi = {10.1371/journal.pcbi.1002533},
editor = {Jensen, Lars Juhl},
issn = {15537358},
journal = {PLoS Computational Biology},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {5},
pages = {e1002533},
publisher = {Public Library of Science},
title = {{Quality of Computationally Inferred Gene Ontology Annotations}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1002533},
volume = {8},
year = {2012}
}