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Evaluating Computational Gene Ontology Annotations.

  • Škunca N Department of Computer Science, ETH Zurich, Universitätstrasse 19, 8092, Zurich, Switzerland. nskunca@gmail.com.
  • Roberts RJ New England Biolabs, 240 County Road, Ipswich, MA, 01938, USA.
  • Steffen M Department of Biomedical Engineering, Boston University, Boston, MA, USA.
  • 2016-11-05
Published in:
  • Methods in molecular biology (Clifton, N.J.). - 2017
Annotation
Evaluation
Function
Gene ontology
Prediction
Tools
Animals
Computational Biology
Computer Simulation
Databases, Genetic
Gene Ontology
Genome
Humans
Models, Biological
Molecular Sequence Annotation
Proteins
English 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.
Language
  • English
Open access status
hybrid
Identifiers
Persistent URL
https://sonar.ch/global/documents/127140
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