Location: Corn Insects and Crop Genetics Research2013 Annual Report
1a. Objectives (from AD-416):
Objective 1: Implement web-accessible computational and visualization tools, including semantic web technologies, to enable comparison and transfer of agronomically important genetic information among soybean and other legume and related dicot species. Objective 2: Continue to curate and enhance SoyBase and the Soybean Breeder’s Toolbox (SBT), more fully integrating the genetic, phenotypic, physical map, and whole-genome sequence data from soybean and other legumes. Objective 3: Coordinate the quality assembly and annotation of the soybean whole-genome sequence.
1b. Approach (from AD-416):
Soybean ontologies will be prepared to describe selected data types from the Soybean Breeders Toolbox (SBT). Data exchange descriptions (“RDF graphs”) will be developed to allow integration of the data into the Virtual Plant Information Network (VPIN). To let researchers transparently find, retrieve, or apply analytical methods to data contained in the SBT, web services will be developed to make these services accessible through a single portal. Soybase and the SBT will be maintained and updated with new data classes as needed. The Williams 82 physical map and the soybean whole genome sequence, new sequence-based data types in SoyBase, and comparative data from other legume will be integrated and displayed. The project works closely with DOE-JGI to enhance the quality of the soybean whole-genome sequence assembly. This will include analysis of sequence-based genetic markers, comparative analyses with other genomes, and various informatic analyses. BL1; Recertified August 20, 2007.
3. Progress Report:
Soybean genomics and SoyBase. The U.S. soybean crop, valued in excess of $35 billion (USDA-NASS), depends on continued breeding improvements in order to achieve yield gains and avoid losses due to pathogens and environmental stresses. The USDA-ARS soybean genetics database, http://soybase.org, provides access to the complete genome sequence for soybean, as well as to predicted genes, markers, valuable traits and their locations, and many other genetic features. We have worked with the Department of Energy-Joint Genome Institute (DOE-JGI), the Hudson Alpha Institute, and National Center for Biotechnology Information (NCBI), to assess and improve gene predictions for soybean. These gene models have been incorporated into the genome browser at SoyBase, along with detailed report pages for all gene predictions. A translation tool allows researchers to find and compare soybean gene predictions from older and newer research. The SoyBase database continues to be actively extended through addition of publications that describe the locations of traits, genes, and features of interest. A new interface provides access to soybean mutant and gene knockout data (http://www.soybase.org/mutants/index.php). This enables researchers to browse through images of known mutants, or to find plants with known genetic lesions. A new version of SoyCyc (soybean metabolic database), based on the latest plant metabolic pathways, has also been incorporated into SoyBase. Crop legume genomics, the Legume Information System, and allied databases. Approximately two dozen species in the bean and pea family are grown as protein-rich crops. We have worked in the past year with international collaborators to assemble, analyze, and publish the genome sequence of chickpea, and have developed a genome browser to provide access to the chickpea genome, genes, and features (http://cicar.comparative-legumes.org). We have also worked with U.S. collaborators to assemble and analyze the genome of common bean, and have developed a genome browser to provide access to the chickpea genome (http://phavu.comparative-legumes.org). Additionally, we have built a new website and database for the U.S. and international peanut breeding community (http://peanutbase.org). These Web resources will enable plant breeders and researchers to more rapidly develop new crop varieties with favorable yield, disease resistance, or stress tolerance characteristics.
1. Improved genome assemblies and gene predictions for soybean, common bean, and chickpea. Global demand continues to increase for protein-rich, nutrient dense foods and oil crops. Legume crops such as soybean, common bean, peanut, and chickpea fill this role, partly due to their ability to work with soil bacteria to convert and use atmospheric nitrogen as a natural fertilizer. These crops all, however, face yield challenges due to pathogens and temperature and water stress. ARS researchers in Ames, IA have worked over the past year with other U.S. and international researchers to help assemble and analyze the genome sequences of common bean and chickpea; to revise the genome assembly and gene predictions for soybean; and to initiate genome sequencing of peanut. These genome sequences contain the complete collection of genetic information for these crops. This genetic information is essentially the “blue-print” that controls how each of these plants grow – how it responds to pathogens and stresses; when it flowers; how much seed it produces; the nutritional characteristics of the seeds; etc. The availability of the genome sequences for these legume crops will make it possible for researchers to more rapidly breed varieties that have improved yield, disease resistance, and stress tolerance.
2. Developed on-line tools for determining soybean gene function. A continuing research challenge to soybean breeders to integrate the wealth of genomic data into a coherent plant breeding scheme. A particular difficulty has been determining the function of predicted genes. ARS researchers at Ames, IA, working with other USDA data generators, have developed a comprehensive tool for searching, visualizing and reporting information for plant lines that contain DNA mutations. The mutated plant lines have been extensively characterized in terms of both their growth patterns and the chromosomal locations of the DNA mutations, and the predicted genes contained in DNA that has undergone mutations. The new tools in SoyBase enable users to explore and use these data and associations. This information will be used by breeders and other researchers to aid in breeding improved soybean varieties.
Varshney, R.K., Song, C., Saxena, R.K., Azam, S., Yu, S., Sharpe, A.G., Cannon, S.B., Rosen, B., Tar'An, B., Millan, T., et al. 2013. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nature Biotechnology. 31:240-246.
Tian, Z., She, M., Zhao, M., Du, J., Cannon, S.B., Liu, X., Xu, X., Lam, H., Ma, J. 2012. Genome-Wide characterization of nonreference transposons reveals evolutionary propensities of transposons in soybean. The Plant Cell. 24(11): 4422-4436.
Cannon, S.B. 2013. The model legume genomes. In: Rose, R.J., editor. Legume Genomics: Methods and Protocols. Heidelberg, NY: Springer. p. 1-14.