The generation challenge programme platform: semantic standards and workbench for crop science.

doi:10.1155/2008/369601

Benchmarking database systems for genomic selection implementation

Motivation: With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results: We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix

Suppression of cell wall-related genes associated with stunting of Oryza glaberrima infected with Rice tungro spherical virus

Rice tungro disease is a complex disease caused by the interaction between Rice tungro bacilliform virus and Rice tungro spherical virus (RTSV). RTSV alone does not cause recognizable symptoms in most Asian rice (Oryza sativa) plants, whereas some African rice (O. glaberrima) plants were found to become stunted by RTSV. Stunting of rice plants by virus infections usually accompanies the suppression of various cell wall-related genes. The expression of cell wall-related genes was examined in O. glaberrima and O. sativa infected with RTSV to see the relationship between the severity of stunting and the suppression of cell wall-related genes by RTSV. The heights of four accessions of O. glaberrima were found to decline by 14 to 34% at 28 days post-inoculation (dpi) with RTSV, whereas the height reduction of O. sativa plants by RTSV was not significant. RTSV accumulated more in O. glaberrima plants than in O. sativa plants, but the level of RTSV accumulation was not correlated with the degree of height reduction among the four accessions of O. glaberrima. Examination for expression of genes for cellulose synthase A5 (CESA5) and A6 (CESA6), cellulose synthase-like A9 (CSLA9) and C7, and -expansin 1 (expansin 1) and 15 precursors in O. glaberrima and O. sativa plants between 7 and 28 dpi with RTSV showed that the genes such as those for CESA5, CESA6, CSLA9, and expansin 1were more significantly suppressed in stunted plants of O. glaberrima at 14 dpi with RTSV than in O. sativa, suggesting that stunting of O. glaberrima might be associated with these cell wall-related genes suppressed by RTSV. Examination for expression of these genes in O. sativa plants infected with other rice viruses in previous studies indicated that the suppression of the expansin 1 gene is likely to be a signature response commonly associated with virus-induced stunting of Oryza species. These results suggest that stunting of O. glaberrima by RTSV infection might be associated with the suppression

Revealing sequence variation patterns in rice with machine learning methods

Motivation The major breakthrough at the turn of the millennium was the completion of genome sequences for individuals from many species, including human, worm and rice. More recently, it has also been important to describe sequence variation within one species, providing the first step towards the linkage of genetic variation to traits. Today, rice is the most important source for human caloric intake, making up 20% of the calorie supply and feeding millions of people daily. The more detailed understanding and findings on the molecular assembly of phenotypic rice varieties will therefore be essential for future improvement in rice cultivation and breeding. In order to reveal patterns of sequence variation in Oryza sativa (rice), the non-repetitive portion of the genomes of 20 diverse rice cultivars was resequenced, in collaboration with Perlegen Sciences, Inc., using a high-density oligonucleotide microarray technology. Methods Based on experience gained in polymorphism studies for Arabidopsis thaliana [1] we developed a method for identifying single nucleotide polymorphisms (SNPs) from the array data using Support Vector Machines (SVMs). In a two-layered approach we trained SVMs to discriminate between SNP and non-SNP positions using information from each cultivar and, in a second step, across all cultivars. Wherever several SNPs or deletion/insertion polymorphisms occur in close vicinity, the hybridisation is suppressed and SNP calling in these regions becomes infeasible. We therefore adapted a machine learning method for sequence segmentation [2, 3] to predict highly polymorphic regions in O. sativa (cf. Figure 1). These regions can then be analysed in more detail using alternative experimental techniques

Implementation of genomic selection in maize and chickpea breeding programs in Africa and South Asia

The Excellence in Breeding Platform is a CGIAR initiative to modernize breeding programs serving the developing world. A brief overview of the platform will be provided, with discussion focused on approaches to data management, cost effective genotyping, data analytics, and breeding strategies for the implementation of genomic selection. Collaborations with the Genomic Open-source Breeding Informatics Initiative (GOBii) on the implementation of genomic selection for CIMMYT Maize and ICRISAT Chickpea breeding programs will be presented and the path forward for routine implementation of genomic selection discussed

Genomewide SNP variation reveals relationships among landraces and modern varieties of rice

Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene–trait relationships and their use for future rice improvement

The generation challenge programme platform: semantic standards and workbench for crop science.

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