ArticlePloS one2013
Systematic analysis of experimental phenotype data reveals gene functions.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12.G3 (Bethesda, Md.) · 2021Article
- Ontology-based validation and identification of regulatory phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Integrating phenotype ontologies with PhenomeNET.Journal of biomedical semantics · 2017Article
- Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics.Journal of integrative bioinformatics · 2017Article
- Semantic prioritization of novel causative genomic variants.PLoS computational biology · 2017Article
- Reporting phenotypes in mouse models when considering body size as a potential confounder.Journal of biomedical semantics · 2016Review
- The integrin-adhesome is required to maintain muscle structure, mitochondrial ATP production, and movement forces in Caenorhabditis elegans.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2015Article
- Prediction of Genetic Interactions Using Machine Learning and Network Properties.Frontiers in bioengineering and biotechnology · 2015Review
- Similarity-based search of model organism, disease and drug effect phenotypes.Journal of biomedical semantics · 2015Article
- Linking gene expression to phenotypes via pathway information.Journal of biomedical semantics · 2015Article
- Methodology for the inference of gene function from phenotype data.BMC bioinformatics · 2014Article
- Analyzing gene expression data in mice with the Neuro Behavior Ontology.Mammalian genome : official journal of the International Mammalian Genome Society · 2014Article
- Genetic interaction networks: better understand to better predict.Frontiers in genetics · 2013Review
- The Drosophila phenotype ontology.Journal of biomedical semantics · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.