Award details

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

Reference	EGA17717
Principal Investigator / Supervisor	Professor Trevor Wang
Co-Investigators / Co-Supervisors	Dr Matthew Hills, Dr Stephen Rawsthorne
Institution	John Innes Centre
Department	Metabolic Biology
Funding type	Research
Value (£)	200,400
Status	Completed
Type	Research Grant
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. (Joint with grants 17716, 17718 and 17719).

Summary

unavailable

Committee	Closed Committee - Genes & Developmental Biology (GDB)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	Exploiting Genomics: Agri, Food and Environment (EGA) [2001]
Funding Scheme	X – not Funded via a specific Funding Scheme

Associated awards:

EGA17716 Hierarchical plant metabolomics for gene function and mode of action discovery

EGA17718 Heirachical plant metabolomics for gene function and mode of action

EGA17719 Hierarchical plant metabolomics for gene function and mode of action (MOA)

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