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Abstract Detail

Genomics / Proteomics

Eilers, Robert J. [1].

Characterizing Biochemical Phenotypes through a Metabolite Analysis Platform: Research Communication Enabled through Public Ontologies and Controlled Vocabularies.

By integrating advanced technologies and process components, we have built an analytical biochemistry platform capable of high-throughput generation of biochemical phenotypes for plants. Critical functions within our research platform include experiment planning, process and quality control, sample collection and preparation, lab analyses, data management, data analysis, data integration and data mining. We have found a confluence of key data components developing between the data management tools we use to effectively conduct our research and tools we use to effectively communicate data and results with our internal and external collaborators. A review of sources of variation in experimental processes has helped us build a new operational framework based on ontologies, which provide definition and relationship information, and controlled vocabularies which provide essential data quality. We have also identified information gaps where opportunities exist for better capture, linkage and integration of correlative or contextual data. One application of ontology development methods for us has been the development of conceptual frameworks through which biological paradigms can be developed and shared. These frameworks serve as a knowledge base for our research teams. This poster will describe our analytical platform, its processes, and how the efficient use of ontologies and controlled vocabularies enable high quality data and communication.

Broader Impacts:

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1 - Monsanto Company, Agriculture Biotechnology, 700 Chesterfield Parkway West , Chesterfield, MO, 63017, USA

metabolite analysis
controlled vocabulary
analytical biochemistry
biochemical phenotypes.

Presentation Type: Poster:Posters for Topics
Session: P
Location: Grand Salon A - D/Riverside Hilton
Date: Monday, July 29th, 2013
Time: 5:30 PM
Number: PGP003
Abstract ID:426
Candidate for Awards:None

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