Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Genome-wide association analysis of bacterial blight resistance to nine Xoo strains in 198 indica genotypes based on Efficient Mixed-Model Association eXpedited Model (EMMAX). Manhattan plots for nine Xoo strains (a) PXO61, (b) PXO86, (c) PXO79, (d) PXO71, (e) PXO112, (f) PXO99, (g) PXO339, (h) PXO349, and (i) PXO341. X-axis shows the SNPs along each chromosome; y axis is the − log10 (P-value) for the association. Significant SNPs are those beyond the red line having P-value < 1 × 10 −5. Quantile-quantile plots for nine Xoo strains (j) PXO61, (k) PXO86, (l) PXO79, (m) PXO71, (n) PXO112, (o) PXO99, (p) PXO339, (q) PXO349, and (r) PXO341. (PPTX 521 kb

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

Crop Ontology Governance and Stewardship Framework

A governance & stewardship framework for the Crop Ontology Project is required as this is a collaborative tool developed by a Community of Practice. Over the last 12 years of its existence, it has increased significantly in scope and use. Collecting and storing plant trait data and annotating the data with ontology terms is widely accepted by the crop science community to be critical to enable data interoperability and interexchange through tools such as the Breeding API (BrAPI). The Crop Ontology Community of Practice is organised around roles, curation principles and validation processes that require a formal description. A governance framework is defined by the various actors involved in the asset’s design, development and maintenance. It is complemented by a quality assurance process to ensure that trust levels, value creation, and sustainability objectives meet appropriate quality levels. The general principles underlying data governance are integrity, transparency, accountability and ownership, stewardship, standardization, change management and a robust data audit

The ontologies community of practice: a CGIAR initiative for Big Data in agrifood systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams

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.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijpg/

SNP-Seek II: A resource for allele mining and analysis of big genomic data in Oryza sativa

The 3000 Rice Genomes Project generated a large dataset of genomic variation to the world’s most important crop, Oryza sativa L. Using the Burrows-Wheeler Aligner (BWA) and the Genome Analysis Toolkit (GATK) variant calling on this dataset, we identified ∼40 M single-nucleotide polymorphisms (SNPs). Five reference genomes of rice representing the major variety groups were used: Nipponbare (temperate japonica), IR 64 (indica), 93–11 (indica), DJ 123 (aus), and Kasalath (aus). The results are accessible through the Rice SNP-Seek Database (http://snp-seek.irri.org) and through web services of the application programming interface (API). We incorporated legacy phenotypic and passport data for the sequenced varieties originating from the International Rice Genebank Collection Information System (IRGCIS) and gene models from several rice annotation projects. The massive genotypic data in SNP-Seek are stored using hierarchical data format 5 (HDF5) files for quick retrieval. Germplasm, phenotypic, and genomic data are stored in a relational database management system (RDBMS) using the Chado schema, allowing the use of controlled vocabularies from biological ontologies as query constraints in SNP-Seek. In this paper, we discuss the datasets stored in SNP-Seek, architecture of the database and web application, interoperability methodologies in place, and discuss a few use cases demonstrating the utility of SNP-Seek for diversity analysis and molecular breeding

Additional file 1: Table S1. of Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Information on the 285 diverse genotypes used in this study. (XLSX 34 kb

Additional file 11: Table S5. of Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Comparison of allele frequency of SNPs overlapping/flanking Xa genes between GWAS 198 indica genotypes and 1788 indica subset in 3 k genomes. (XLSX 46 kb

Genomic variation in 3,010 diverse accessions of Asian cultivated rice

Here we analyse genetic variation, population structure and diversity among 3,010 diverse Asian cultivated rice (Oryza sativa L.) genomes from the 3,000 Rice Genomes Project. Our results are consistent with the five major groups previously recognized, but also suggest several unreported subpopulations that correlate with geographic location. We identified 29 million single nucleotide polymorphisms, 2.4 million small indels and over 90,000 structural variations that contribute to within- and between-population variation. Using pan-genome analyses, we identified more than 10,000 novel full-length protein-coding genes and a high number of presence-absence variations. The complex patterns of introgression observed in domestication genes are consistent with multiple independent rice domestication events. The public availability of data from the 3,000 Rice Genomes Project provides a resource for rice genomics research and breeding