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The Generation Challenge programme is a global crop research consortium directed toward …

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Results and Discussion
- The Generation Challenge Programme Platform: Semantic Standards and Workbench for Crop Science

The GCP consortium was formally established in 2003. The first meeting of the bioinformatics and crop informatics development team of the GCP, designated as Subprogramme 4, was hosted in Rome, in February 2004. The general user needs and project goals were coarsely mapped out at this meeting, with some considerable differences in opinion voiced at how to construct the required informatics framework for the GCP. In May 2004, a smaller team of software experts met in Mexico to discuss project management, identify key user needs and platform requirements, and make some initial progress in the design of the system. Key decisions at this latter meeting were the adoption of the “model-driven architecture” paradigm for system development and to embrace web services as a key technology for global integration of systems. Numerous development meetings have been convened annually since these initial meetings to further refine and advance the design and implementation of the platform.

In particular, a milestone review of the GCP domain model and initial software systems using the model was held in Pretoria, South Africa in March 2006. Since that time, a number of early release versions of software systems based on GCP platform technology have become available, generally documented at and publicly downloadable from various CropForge projects. A special “communications” project for GCP-specific projects is also available on CropForge at the to further inform prospective users on the variety of such GCP software tools now available, and provide a venue for user discussions and feedback about the tools.

So, what can I do with the GCP platform?
The vision of the platform development team of the bioinformatics and crop informatics subprogramme of the GCP is to establish a truly easy to use but extensible workbench providing interoperability and enhanced data access across all GCP partner sites and, later, across the global crop research community. As indicated above, the GCP domain model has a scope of data type coverage that spans most of the pertinent scientific data types found in crop research from upstream laboratory experiments through germplasm manipulations, in a georeferenced characterized field setting. The diversity of potential data sources and analysis tools is similarly large. What the platform facilitates is transparent data flows between such data sources and tools, whether from locally administered databases or remote Internet-connection resources.

In this light, a number of practical “use cases” may be described in general terms, as a series of data manipulation steps, to highlight some of the anticipated usage of the platform. As an indication of the data retrieval and analysis scope of the GCP platform, we describe a general integrative use case here below, in terms of a series of defined steps.

General GCP platform analysis use case for crop improvement


  • Retrieve the list of all genetic maps that include a quantitative trait locus (QTL) for a specified trait.
  • Retrieve selected maps in the list, from a project database or source file containing such maps.
  • Load this into a suitable mapping tool (e.g., the comparative map and trait visualization tool, CMTV).
  • Extract the pairs of flanking markers for the QTL.
  • From a second (crop) database, retrieve the list of all germplasm that have been genotyped with these flanking markers.
  • Retrieve all the pertinent passport, genotype, and phenotype information about the germplasm in the list.
  • In parallel to the steps (5) and (6), if available, retrieve any gene locus candidates within (genetic/physical/sequence) map intervals which are defined by flanking markers which are molecular sequence based.
  • Retrieve gene functional information about the gene loci compiled in step (7).
  • Retrieve the alleles of “interesting” genes from (8), in the list of germplasm identified in step (5).
  • Plot germplasm passport, genotype, and phenotype information on geographical information maps.
  • Retrieve information about the environmental characteristics of the geographical regions identified in step (10).
  • Identify germplasm, for further detailed evaluation, which appears to be adapted to target environments, which have promising phenotypic values identified in step (6) and which contains target alleles of gene loci identified in step (9).
  • Identify genotyping (marker) systems potentially available from step (9), for marker assisted selected transfer of target traits from identified germplasm to additional germplasm targets.

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