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Methods Mol Biol. 2017;1446:97-109.

Evaluating Computational Gene Ontology Annotations.

Author information

1
Department of Computer Science, ETH Zurich, Universitätstrasse 19, 8092, Zurich, Switzerland. nskunca@gmail.com.
2
SIB Swiss Institute of Bioinformatics, Universitätstr. 19, 8092, Zurich, Switzerland. nskunca@gmail.com.
3
University College London, Street Gower St, WC1E 6BT, London, UK. nskunca@gmail.com.
4
New England Biolabs, 240 County Road, Ipswich, MA, 01938, USA.
5
Department of Biomedical Engineering, Boston University, Boston, MA, USA.
6
Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.

Abstract

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.

KEYWORDS:

Annotation; Evaluation; Function; Gene ontology; Prediction; Tools

PMID:
27812938
DOI:
10.1007/978-1-4939-3743-1_8
[Indexed for MEDLINE]

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