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United States Department of Agriculture

Agricultural Research Service

Research Project: THE MAIZE GENETICS AND GENOMICS DATABASE
2013 Annual Report


1a.Objectives (from AD-416):
Objective 1: Integrate new maize genetic and genomic data into the database. Objective 2: Provide community support services, such as lending help to the community of maize researchers with respect to developing and publicizing a set of guidelines for researchers to follow to ensure that their data can be made available through MaizeGDB; coordinating annual meetings; and conducting elections and surveys.


1b.Approach (from AD-416):
Data integration: To best leverage the cooperative spirit of the maize community, we will encourage the use of a set of Community Curation Tools to enable researchers to deposit their own small datasets into the database directly. To reduce secondary curation of data, we will generate standards for data deposition and define file formats for automated inputs of large datasets and will work in concert with maize researchers as they devise methods for initial data storage so that the data transition to MaizeGDB is simplified. Shift to a sequence-centric paradigm: To allow researchers to visualize a gene within its genomic context and to visualize gene products within the context of relevant metabolic pathways annotated with ontology terms, we will develop new views of the data. We will link sequence data to relevant datasets, especially the centrally important maps such as (1) IBM2, (2) its neighbors, and (3) the new maize diversity map. We also will incorporate a genome browser into the MaizeGDB product to create a view that includes all major genome assemblies and predicted gene structures and displays the official maize genome annotation. Community coordination: We will conduct critical maize genetics community functions including coodinating and conducting annual meetings, elections, and surveys and preparing the Maize Newsletter.


3.Progress Report:
ARS scientists working on the Maize Genetics and Genomics Database (MaizeGDB) in Ames, Iowa and Columbia, Missouri improved tools that make the maize genome sequence useful for investigative research and crop improvement. Genome sequences served include the B73 reference genome as well as the Mexican Palomero Toluqueno and skim sequence of various other lines that represent the broad diversity represented by the Zea genus (i.e., maize and its near relatives). Tools deployed include the Locus Lookup Tool, which enables the integration of genetic information with genome sequence representations, as well as a tool that shows regions of the genome assembly that are incongruous with genetic maps of the same genomic regions. Members of the MaizeGDB team improved phenotypic descriptions by applying standardized terms that are used to describe traits in all flowering plants. Use of these terms enables cross-species queries of traits and phenotypes shared among various plants, which enables multiple plant data repositories to be searched simultaneously. This allows researchers to leverage research outcomes from, e.g., wheat investigations to hypothesize function in maize. This effort is one example of how scientists at MaizeGDB support Open Data for agriculture. Another is the deployment of the POPcorn resource that enables researchers to simultaneously search multiple cooperator websites to return genomic information for maize as a single results set, which saves researchers’ time and keeps them from having to keep track of multiple repository locations. A high-availability infrastructure that obviates the need for disaster recovery was deployed to ensure availability of MaizeGDB for researchers’ use. To enable a better understanding of how the genes in a plant define the potential phenotypes that will be observed in farmers’ fields, we deployed two pathway view tool suites: MaizeCyc and CornCyc. These resources help researchers determine which genes and pathways to select for targeted crop improvement. Having met all milestones for the project by year four of the project’s five-year time frame, the graphical user interface of MaizeGDB was redesigned over the course of years four and five. The redesigned website is in the process of final deployment. Work carried out by the MaizeGDB team has resulted in improved communication among maize researchers worldwide, increased ability to document the results of experiments, and increased availability of information relative to high impact research.


4.Accomplishments
1. New DNA-based markers made available for crop improvement. ARS researchers in Ames, Iowa, in collaboration with ARS researchers in Columbia, Missouri and Ithaca, New York, transferred genetic and genomic data to MaizeGDB for a commercially available microarray. This array supports high throughput genotyping of maize and links genome sequence to phenotypes. Data transferred to MaizeGDB include information that enables researchers to determine where genes are located on chromosomes and data are stored to enable interoperability with other database groups including the National Institutes of Health database National Center for Biotechnology Information (NCBI). Markers have been validated using germplasm adapted to the U.S. Corn Belt, other regions of the Americas, Europe, China, and Africa, and also to wild relatives of maize. Making these data accessible at MaizeGDB transfers a relatively inexpensive technology that helps plant researchers and breeders to adopt modern strategies in crop improvement.

2. New DNA-based markers made available for crop improvement. ARS researchers in Ames, Iowa, in collaboration with ARS researchers in Columbia, Missouri and Ithaca, New York, transferred genetic and genomic data to MaizeGDB for the commercially available Illumina MaizeSNP50 BeadChip array. This array supports high throughput genotyping of maize and links genome sequence to phenotypes. Data transferred to MaizeGDB include genetic map coordinates, B73 reference genome sequence coordinates, and links to the National Center for Biotechnology Information database dbSNP. The array is exceptionally robust. Markers have been validated using germplasm adapted to the U.S. Corn Belt, other regions of the Americas, Europe, China, and Africa, and also to wild relatives of maize. Making these data accessible at MaizeGDB transfers a relatively inexpensive technology that helps plant researchers and breeders to adopt modern strategies in crop improvement.

3. Predicting how genes function helps researchers create crop improvement strategies. ARS researchers in Ames, Iowa, in collaboration with ARS scientist in Columbia, Missouri, Albany, California, and Cold Spring Harbor, New York as well as collaborators at Iowa State University, Ames, Iowa and Stanford University, Stanford, California, completed their work of expanding the MaizeGDB’s Metabolic Network Resources (MaizeCyc and CornCyc). High-quality representations of agronomically important pathways were added to the database based on published literature. Analyses conducted confirm that MaizeCyc is a larger dataset whereas CornCyc is a smaller, high-quality resource. These pathway tools help researchers determine which genes and pathways to investigate and target for crop improvement.


Review Publications
Robbins, R.J., Amaral-Zettler, L., Bik, H., Blum, S., Edwards, J., Field, D., Garrity, G., Gilbert, J.A., Kottmann, R., Krishtalka, L., Lawrence, C.J. 2012. RCN4GSC workshop report: managing data at the interface of biodiversity and (meta)genomics, March 2011. Standards in Genomic Sciences. 7(1):159-165.

Andorf, C.M., Honavar, V., Sen, T.Z. 2013. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks. PLoS One. 8(2):e56833.

Ghaffari, R., Cannon, E.K., Kanizay, L.B., Lawrence, C.J., Dawe, K.R. 2013. Maize chromosomal knobs are located in gene-dense areas and suppress local recombination. Chromosoma. 122(1-2):67-75.

Lushbough, C.M., Bergman, M.K., Lawrence, C.J., Jennewein, D., Brendel, V. 2008. Implementing bioinformatic workflows within the BioExtract Server. International Journal of Computational Biology and Drug Design. 1(3):302-312.

Monaco, M.K., Sen, T.Z., Dharmawardhana, P., Ren, L., Schaeffer, M.L., Amarasinghe, V., Thomason, J., Harper, E.C., Gardiner, J.M., Lawrence, C.J., Ware, D., Jaiswal, P., Naithani, S., Cannon, E. 2013. Maize metabolic network construction and transcriptome analysis. The Plant Genome. 6(1):DOI:10.3835/plantgenome2012.09.0025.

Last Modified: 11/28/2014
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