Systematic analysis of experimental phenotype data reveals gene functions.

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 t...

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Autores principales:	Robert Hoehndorf, Nigel W Hardy, David Osumi-Sutherland, Susan Tweedie, Paul N Schofield, Georgios V Gkoutos
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c41b97e356b34816ba134d75b2563a48
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spelling	oai:doaj.org-article:c41b97e356b34816ba134d75b2563a482021-11-18T07:49:11ZSystematic analysis of experimental phenotype data reveals gene functions.1932-620310.1371/journal.pone.0060847https://doaj.org/article/c41b97e356b34816ba134d75b2563a482013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23626672/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203High-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.Robert HoehndorfNigel W HardyDavid Osumi-SutherlandSusan TweediePaul N SchofieldGeorgios V GkoutosPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e60847 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Robert Hoehndorf Nigel W Hardy David Osumi-Sutherland Susan Tweedie Paul N Schofield Georgios V Gkoutos Systematic analysis of experimental phenotype data reveals gene functions.
description	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.
format	article
author	Robert Hoehndorf Nigel W Hardy David Osumi-Sutherland Susan Tweedie Paul N Schofield Georgios V Gkoutos
author_facet	Robert Hoehndorf Nigel W Hardy David Osumi-Sutherland Susan Tweedie Paul N Schofield Georgios V Gkoutos
author_sort	Robert Hoehndorf
title	Systematic analysis of experimental phenotype data reveals gene functions.
title_short	Systematic analysis of experimental phenotype data reveals gene functions.
title_full	Systematic analysis of experimental phenotype data reveals gene functions.
title_fullStr	Systematic analysis of experimental phenotype data reveals gene functions.
title_full_unstemmed	Systematic analysis of experimental phenotype data reveals gene functions.
title_sort	systematic analysis of experimental phenotype data reveals gene functions.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c41b97e356b34816ba134d75b2563a48
work_keys_str_mv	AT roberthoehndorf systematicanalysisofexperimentalphenotypedatarevealsgenefunctions AT nigelwhardy systematicanalysisofexperimentalphenotypedatarevealsgenefunctions AT davidosumisutherland systematicanalysisofexperimentalphenotypedatarevealsgenefunctions AT susantweedie systematicanalysisofexperimentalphenotypedatarevealsgenefunctions AT paulnschofield systematicanalysisofexperimentalphenotypedatarevealsgenefunctions AT georgiosvgkoutos systematicanalysisofexperimentalphenotypedatarevealsgenefunctions
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Systematic analysis of experimental phenotype data reveals gene functions.

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