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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #383609

Research Project: MaizeGDB: Enabling Access to Basic, Translational, and Applied Research Information

Location: Corn Insects and Crop Genetics Research

Title: A pan-genomic approach to genome databases using maize as a model system

item Woodhouse, Margaret
item Cannon, Ethalinda
item Portwood, John
item Harper, Elisabeth
item GARDINER, JACK - University Of Missouri
item SCHAEFFER, MARY - University Of Missouri
item Andorf, Carson

Submitted to: BMC Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/11/2021
Publication Date: 8/20/2021
Publication URL:
Citation: Woodhouse, M.H., Cannon, E.K., Portwood II, J.L., Harper, E.C., Gardiner, J.M., Schaeffer, M.L., Andorf, C.M. 2021. A pan-genomic approach to genome databases using maize as a model system. Biomed Central (BMC) Plant Biology. 21. Article 385.

Interpretive Summary: Given the diversity of individuals in any species, a single genome from one individual is not enough to represent the complexity of that species. This is true for any species, including maize (corn). Currently, the genomes of over 40 diverse species in maize have been sequenced, and these genomes together better represent the diversity of maize than any of these genomes alone. With this in mind, the Maize Genetics and Genomics Database (MaizeGDB) has reformatted its database to efficiently connect these diverse genomes and their functional data so that scientists can quickly and easily compare maize genomes to determine how diverse they are from each other. This helps scientists to understand how maize diversity in different cultivars is related to differences in maize development, disease resistance, and other important agricultural traits.

Technical Abstract: Research in the past decade has demonstrated that a single reference genome is not representative of a species’ diversity. MaizeGDB introduces a pan-genomic approach to hosting genomic data, leveraging the large number of diverse maize genomes and their associated datasets to quickly and efficiently connect genomes, gene models, expression, epigenome, sequence variation, structural variation, transposable elements, and diversity data across genomes so that researchers can easily track the structural and functional differences of a locus and its orthologs across maize. We believe our framework is unique and provides a template for any genomic database poised to host large-scale pan-genomic data.