Evaluating Computational Gene Ontology Annotations [chapter]

Nives Škunca, Richard J. Roberts, Martin Steffen
2016 Msphere  
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
more » ... ty 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.
doi:10.1007/978-1-4939-3743-1_8 pmid:27812938 fatcat:2rz6jdsuizbzxil2hxwlynd2dq