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PLoS One. 2013 Apr 16;8(4):e60847. doi: 10.1371/journal.pone.0060847. Print 2013.

Systematic analysis of experimental phenotype data reveals gene functions.

Author information

1
Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom. rh497@cam.ac.uk

Abstract

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene [Formula: see text] leads to a phenotypic abnormality in a process [Formula: see text], then [Formula: see text] must have been involved in [Formula: see text], either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature.

PMID:
23626672
PMCID:
PMC3628905
DOI:
10.1371/journal.pone.0060847
[Indexed for MEDLINE]
Free PMC Article
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