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Hierarchical plant metabolomics for gene function and mode of action (MOA)

Reference	BBS/E/J/0000A096
Principal Investigator / Supervisor	Professor Trevor Wang
Co-Investigators / Co-Supervisors
Institution	John Innes Centre
Department	John Innes Centre Department
Funding type	Research
Value (£)	93,722
Status	Completed
Type	Institute Project
Start date	03/03/2003
End date	02/03/2006
Duration	36 months

Abstract

Exploitation of genome sequences on public databases requires integrated approaches to determine gene function. Rapid phenotype fingerprinting is essential to exploit reverse genetics resources, such as the ATIS gene tag Arabidopsis mutant collection. We propose a hierarchical metabolomics approach opening with machine learning computational of high-throughput analysis of plant metabolites to facilitate comprehensive gene function determination. A major output will be a metabolome fingerprint database that will provide the basis of future applied post-genomic technologies for high-throughput mode- of-action analysis for agrochemical discovery as well as plant breeding, substantial equivalence testing of GMOs and food quality assessment.

Summary

unavailable

Committee	Closed Committee - Plant & Microbial Sciences (PMS)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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