Ontology-driven International Maize Information System (IMIS) for phenotypic and genotypic data exchange
Shrestha, R.
Ontology-driven International Maize Information System (IMIS) for phenotypic and genotypic data exchange - United Kingdom : Nature Publishing Group : 2010 - 1 Poster
Open Access
The Consultative Group on International Agricultural Research (CGIAR; http://www.cgiar.org/) centres have developed the International Crop Information System (ICIS; http://www.icis.cgiar.org) for the management and integration of global information on genetic resources, and germplasm improvement for any crop. The Maize breeding programs at CIMMYT (http://beta.cimmyt.org/) have different software tools to manage phenotypic, genotypic, and environmental information for their experiments generated worldwide. These tools have the capacity of collecting information in the field, wet lab, and store it into different relational databases. The IMIS (http://imis.cimmyt.org/confluence/display/IMIS/Crop+Finder) is an implementation of the ICIS, which is a computerized database system for general, integrated management and utilization of genealogy, nomenclature, genetic, phenotypic and characterization data for maize. Data exchange within and between databases as well as retrieving information are often hampered by the variability of terms used to describe comparable objects. To overcome this problem, the Crop Ontology (CO) database (http://cropontology.org/) is developed. It provides controlled vocabulary sets for several economically important plant species and facilitates biocurators working in genebanks of plant genetic resources (PGR) and crop breeding data curation and annotation. The maize trait ontology is developed as one of subclasses of CO trait ontology providing standardized trait descriptions, scales and scale values implemented into the IMIS. This ontology-driven IMIS will allow researchers who wish to exploit comparative phenotypic and genotypic information of maize to elucidate functional aspects of each trait.
Text in English
https://doi.org/10.1038/npre.2010.5029.1
Bioinformatics
Botany
Data
Ontology engineering
Ontology-driven International Maize Information System (IMIS) for phenotypic and genotypic data exchange - United Kingdom : Nature Publishing Group : 2010 - 1 Poster
Open Access
The Consultative Group on International Agricultural Research (CGIAR; http://www.cgiar.org/) centres have developed the International Crop Information System (ICIS; http://www.icis.cgiar.org) for the management and integration of global information on genetic resources, and germplasm improvement for any crop. The Maize breeding programs at CIMMYT (http://beta.cimmyt.org/) have different software tools to manage phenotypic, genotypic, and environmental information for their experiments generated worldwide. These tools have the capacity of collecting information in the field, wet lab, and store it into different relational databases. The IMIS (http://imis.cimmyt.org/confluence/display/IMIS/Crop+Finder) is an implementation of the ICIS, which is a computerized database system for general, integrated management and utilization of genealogy, nomenclature, genetic, phenotypic and characterization data for maize. Data exchange within and between databases as well as retrieving information are often hampered by the variability of terms used to describe comparable objects. To overcome this problem, the Crop Ontology (CO) database (http://cropontology.org/) is developed. It provides controlled vocabulary sets for several economically important plant species and facilitates biocurators working in genebanks of plant genetic resources (PGR) and crop breeding data curation and annotation. The maize trait ontology is developed as one of subclasses of CO trait ontology providing standardized trait descriptions, scales and scale values implemented into the IMIS. This ontology-driven IMIS will allow researchers who wish to exploit comparative phenotypic and genotypic information of maize to elucidate functional aspects of each trait.
Text in English
https://doi.org/10.1038/npre.2010.5029.1
Bioinformatics
Botany
Data
Ontology engineering