Systematic analysis of experimental phenotype data reveals gene functions.

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 an

SUBMITTER: Hoehndorf R 

PROVIDER: S-EPMC3628905 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature