Evaluating Computational Gene Ontology Annotations.
Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.
Department of Computer Science, ETH Zurich, Universitätstrasse 19, 8092, Zurich, Switzerland. nskunca@gmail.com.|SIB Swiss Institute of Bioinformatics, Universitätstr. 19, 8092, Zurich, Switzerland. nskunca@gmail.com.|University College London, Street Gower St, WC1E 6BT, London, UK. nskunca@gmail.com.
Nives
N
Škunca
New England Biolabs, 240 County Road, Ipswich, MA, 01938, USA.
Richard J
RJ
Roberts
Department of Biomedical Engineering, Boston University, Boston, MA, USA.|Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.
Martin
M
Steffen
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D063990:Gene Ontology; D016678:Genome; D006801:Humans; D008954:Models, Biological; D058977:Molecular Sequence Annotation / Q000379:methods*; D011506:Proteins / Q000235:genetics / Q000378:metabolism
D011506:Proteins
Annotation; Evaluation; Function; Gene ontology; Prediction; Tools