Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function"
Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Daltons) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of...
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doaj-eedfec6cdde84397aad6a6637ecf87e92020-11-25T01:49:16ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2012-02-01310.3389/fpls.2012.0001517128Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function"Stephanie Michelle Moon Quanbeck0Libuse eBrachova1Alexis A. Campbell2Xin eGuan3Ann ePerera4Kun eHe5Seung Y. Rhee6Preeti eBais7Julie eDickerson8Philip eDixon9Gert eWohlgemuth10Oliver eFiehn11Lenore eBarkan12B. Markus eLange13Insuk eLee14Diego eCortes15Carolina eSalazar16Joel eShuman17Vladimir eShulaev18Vladimir eShulaev19David eHuhman20Lloyd W. Sumner21Mary R. Roth22Ruth eWelti23Hilal eIlarslan24Eve S. Wurtele25Basil J. Nikolau26Iowa State UniversityIowa State UniversityIowa State UniversityIowa State UniversityIowa State UniversityCarnegie Insitution for ScienceCarnegie Insitution for ScienceIowa State UniversityIowa State UniversityIowa State UniversityUniversity of CaliforniaUniversity of CaliforniaWashington State UniversityWashington State UniversityCollege of LIfe Science and Biotechnology, Yonsei UniversityVirginia TechVirginia TechVirginia TechVirginia TechUniversity of North TexasThe Samuel Roberts Noble FoundationThe Samuel Roberts Noble FoundationKansas State UniversityKansas State UniversityIowa State UniversityIowa State UniversityIowa State UniversityMetabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Daltons) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformatists and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function. A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of genes of unknown functions.http://journal.frontiersin.org/Journal/10.3389/fpls.2012.00015/fullArabidopsisMetabolomicsdatabaseFunctional Genomicsgene annotation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stephanie Michelle Moon Quanbeck Libuse eBrachova Alexis A. Campbell Xin eGuan Ann ePerera Kun eHe Seung Y. Rhee Preeti eBais Julie eDickerson Philip eDixon Gert eWohlgemuth Oliver eFiehn Lenore eBarkan B. Markus eLange Insuk eLee Diego eCortes Carolina eSalazar Joel eShuman Vladimir eShulaev Vladimir eShulaev David eHuhman Lloyd W. Sumner Mary R. Roth Ruth eWelti Hilal eIlarslan Eve S. Wurtele Basil J. Nikolau |
spellingShingle |
Stephanie Michelle Moon Quanbeck Libuse eBrachova Alexis A. Campbell Xin eGuan Ann ePerera Kun eHe Seung Y. Rhee Preeti eBais Julie eDickerson Philip eDixon Gert eWohlgemuth Oliver eFiehn Lenore eBarkan B. Markus eLange Insuk eLee Diego eCortes Carolina eSalazar Joel eShuman Vladimir eShulaev Vladimir eShulaev David eHuhman Lloyd W. Sumner Mary R. Roth Ruth eWelti Hilal eIlarslan Eve S. Wurtele Basil J. Nikolau Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" Frontiers in Plant Science Arabidopsis Metabolomics database Functional Genomics gene annotation |
author_facet |
Stephanie Michelle Moon Quanbeck Libuse eBrachova Alexis A. Campbell Xin eGuan Ann ePerera Kun eHe Seung Y. Rhee Preeti eBais Julie eDickerson Philip eDixon Gert eWohlgemuth Oliver eFiehn Lenore eBarkan B. Markus eLange Insuk eLee Diego eCortes Carolina eSalazar Joel eShuman Vladimir eShulaev Vladimir eShulaev David eHuhman Lloyd W. Sumner Mary R. Roth Ruth eWelti Hilal eIlarslan Eve S. Wurtele Basil J. Nikolau |
author_sort |
Stephanie Michelle Moon Quanbeck |
title |
Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" |
title_short |
Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" |
title_full |
Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" |
title_fullStr |
Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" |
title_full_unstemmed |
Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function" |
title_sort |
metabolomics as a hypothesis-generating functional genomics tool for the annotation of arabidopsis thaliana genes of "unknown function" |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2012-02-01 |
description |
Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Daltons) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformatists and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function. A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of genes of unknown functions. |
topic |
Arabidopsis Metabolomics database Functional Genomics gene annotation |
url |
http://journal.frontiersin.org/Journal/10.3389/fpls.2012.00015/full |
work_keys_str_mv |
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