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

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding.  A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform; (ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive,  high-throughput analyses of project data; (iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making

Characterizing genetic diversity and creating novel gene pools in rice for trait dissection and gene function discovery

Rice diversity is the foundation for rice improvement programs. At IRRI, over 100,000 rice accessions are deposited, and intelligent use of this diversity can not only help solve current production problems but also create future production opportunities and tackle climate change challenges. To fully explore and utilize rice diversity, two ingredients are needed: 1 - the genetic blueprints of diverse rice accessions in use, 2 - plant populations with recombined genotypes allowing expression of phenotypic variation and discovery of new genes/QTLs for use in breeding programs. Sequencing of the genomes & obtaining SNP genotypes of many rice accessions is feasible due to decreasing cost of advanced DNA sequencing technologies. Coupled with the creation of populations suitable for trait dissection / phenotyping, discovery of gene functions and allelic variations causal to important agronomic traits becomes possible. This in turn will provide rich biological evidences to the rice/cereal crop genome annotation community

Light-regulated and cell-specific methylation of the maize PEPC promoter

The molecular mechanisms governing PEPC expression in maize remain to be fully defined. Differential methylation of a region in the PEPC promoter has been shown to correlate with transcript accumulation, however, to date, investigations into the role of DNA methylation in maize PEPC expression have relied on the use of methylation-sensitive restriction enzymes. Bisulphite sequencing was used here to provide a single-base resolution methylation map of the maize PEPC promoter. It is shown that four cytosine residues in the PEPC promoter are heavily methylated in maize root tissue. In leaves, de-methylation of these cytosines is dependent on illumination and is coincident with elevated PEPC expression. Furthermore, light-regulated de-methylation of these cytosines occurs only in mesophyll cells. No methylation was discovered in the 0.6 kb promoter required for mesophyll-specific expression indicating that cytosine methylation is not required to direct the cell-specificity of PEPC expression. This raises interesting questions regarding the function of the cell-specific cytosine de-methylation observed in the upstream region of the PEPC promoter

Characterization of statistical features for plant microRNA prediction

<p>Abstract</p> <p>Background</p> <p>Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected.</p> <p>Results</p> <p>The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart.</p> <p>Conclusions</p> <p>In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.</p

Development of a novel data mining tool to find cis-elements in rice gene promoter regions

<p>Abstract</p> <p>Background</p> <p>Information on more than 35 000 full-length <it>Oryza sativa </it>cDNAs, together with associated microarray gene expression data collected under various treatment conditions, has made it feasible to identify motifs that are conserved in gene promoters and may act as <it>cis</it>-regulatory elements with key roles under the various conditions.</p> <p>Results</p> <p>We have developed a novel tool that searches for <it>cis</it>-element candidates in the upstream, downstream, or coding regions of differentially regulated genes. The tool first lists <it>cis-</it>element candidates by motif searching based on the supposition that if there are <it>cis-</it>elements playing important roles in the regulation of a given set of genes, they will be statistically overrepresented and will be conserved. Then it evaluates the likelihood scores of the listed candidate motifs by association rule analysis. This strategy depends on the idea that motifs overrepresented in the promoter region could play specific roles in the regulation of expression of these genes. The tool is designed so that any biological researchers can use it easily at the publicly accessible Internet site <url>http://hpc.irri.cgiar.org/tool/nias/ces</url>. We evaluated the accuracy and utility of the tool by using a dataset of auxin-inducible genes that have well-studied <it>cis-</it>elements. The test showed the effectiveness of the tool in identifying significant relationships between <it>cis-</it>element candidates and related sets of genes.</p> <p>Conclusion</p> <p>The tool lists possible <it>cis-</it>element motifs corresponding to genes of interest, and it will contribute to the deeper understanding of gene regulatory mechanisms in plants.</p

Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature

The ‘Crop Ontology’ database we describe provides a controlled vocabulary for several economically important crops. It facilitates data integration and discovery from global databases and digital literature. This allows researchers to exploit comparative phenotypic and genotypic information of crops to elucidate functional aspects of traits

Development of GCP ontology for sharing crop information.

Poster presented at 3rd international Biocuration Conference. Berlin (Germany). 17 Apr 2009

Development of GCP Ontology for Sharing Crop Information

The Generation Challenge Programme (GCP &#x2013; &#x22;http://www.generationcp.org&#x22;:http://www.generationcp.org) is a globally distributed crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. GCP adopted the development paradigm of a &#x2018;model-driven architecture&#x2019; to achieve the interoperability and integration of diverse GCP data types that are available through distributed data sources and consumed by end-user data analysis tools. Its objective is to ensure semantic compatibility across the Consortium that will lead to the creation of robust global public goods from GCP research results. &#xd;&#xa;&#xd;&#xa;The GCP scientific domain model is an object model that encapsulates key crop science concepts and is documented using Unified Modeling Language (see GCP Models on &#x22;http://pantheon.generationcp.org/index.php&#x22;:http://pantheon.generationcp.org/index.php). &#xd;&#xa;&#xd;&#xa;At the core of the GCP architecture is a scientific domain model, which is heavily parameterized with GCP-indexed ontology terms. The GCP-indexed ontology reuses established international standards where available, converts other publicly available controlled vocabularies into formally managed ontology, and develops novel ontology if no public vocabularies yet exist. General and crop-specific GCP ontologies are being developed by crop teams involving GCP and external scientific experts &#x2013; in particular, for crop-specific ontology relating to plant anatomy, developmental stage, trait and phenotype for selected GCP crops. Crop ontologies are being developed for chickpea, maize, Musa, potato, rice, sorghum and wheat. The Bioversity crop descriptor lists already loaded into OBO format files provide the primary structure to develop the crop ontologies. Then, terms to be mapped to the ontologies are extracted from the crop databases where trait values have been stored by crop scientists. These sources allow the ontology teams to identify the most commonly used concept names and their interrelations. Experts validate the selection of keywords that will build the controlled vocabulary. &#xd;&#xa;&#xd;&#xa;These GCP ontologies will allow researchers and end users to query keywords related to traits, plant structure, growth stage, and molecular function, and link them to associated phenotyping and genotyping data sets including data on germplasm, crop physiology, geographic information, genes, QTL, etc. To reach that stage, the crop ontologies will be integrated into the data-entry user interface or data templates as picklists facilitating data annotation and submission of new terms. In addition, the GCP ontologies will be integrated with Plant Ontology (PO) and Gramene (Trait Ontology, TO; Environment Ontology, EO) to develop a common, internationally shared crop trait and anatomy ontology. The team will initiate collaboration with SONet (Scientific Observations Network) and OBOE (Extensible Observation Ontology), which proposed to integrate the GCP ontology as a study case.&#xd;&#xa;The Open Biomedical Ontologies (OBO) edit tool has been used to develop the ontologies for rice, wheat and maize traits, which are currently available at &#x22;http://cropforge.org/projects/gcpontology/&#x22;:http://cropforge.org/projects/gcpontology/ . The crop-specific work plans and ontologies related to other materials are published at &#x22;http://pantheon.generationcp.org&#x22;:http://pantheon.generationcp.org. &#xd;&#xa;The development and curation of general-purpose ontologies will be continued and made available on the Pantheon and CropForge websites