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

Chemical- and irradiation-induced mutants of indica rice IR64 for forward and reverse genetics

IR64, the most widely grown indica rice in South and Southeast Asia, possesses many positive agronomic characteristics (e.g., wide adaptability, high yield potential, tolerance to multiple diseases and pests, and good eating quality,) that make it an ideal genotype for identifying mutational changes in traits of agronomic importance. We have produced a large collection of chemical and irradiation-induced IR64 mutants with different genetic lesions that are amenable to both forward and reverse genetics. About 60,000 IR64 mutants have been generated by mutagenesis using chemicals (diepoxybutane and ethylmethanesulfonate) and irradiation (fast neutron and gamma ray). More than 38,000 independent lines have been advanced to M4 generation enabling evaluation of quantitative traits by replicated trials. Morphological variations at vegetative and reproductive stages, including plant architecture, growth habit, pigmentation and various physiological characters, are commonly observed in the four mutagenized populations. Conditional mutants such as gain or loss of resistance to blast, bacterial blight, and tungro disease have been identified at frequencies ranging from 0.01% to 0.1%. Results from pilot experiments indicate that the mutant collections are suitable for reverse genetics through PCR-detection of deletions and TILLING. Furthermore, deletions can be detected using oligomer chips suggesting a general technique to pinpoint deletions when genome-wide oligomer chips are broadly available. M4 mutant seeds are available for users for screening of altered response to multiple stresses. So far, more than 15,000 mutant lines have been distributed. To facilitate broad usage of the mutants, a mutant database has been constructed in the International Rice Information System (IRIS; http: //www.iris.irri.org) to document the phenotypes and gene function discovered by users

The Generation Challenge Programme comparative plant stress-responsive gene catalogue

The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit (‘drought’). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at http://dayhoff.generationcp.org

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

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/

doi:10.1093/nar/gkm798 The Generation Challenge Programme comparative

plant stress-responsive gene catalogu

Rice Information System (IRIS)

Vol. 19 Suppl. 1 2003, pages i63–i6