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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #355949

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

Location: Corn Insects and Crop Genetics Research

Title: MaizeGDB: How phenotype curation has co-evolved with genomic representations

Author
item Harper, Elisabeth
item Gardiner, Jack - University Of Missouri
item Portwood, John
item Cho, Kyoung Tak - Iowa State University
item Woodhouse, Margaret - Iowa State University
item Cannon, Ethalinda
item Lawrence-dill, Carolyn - Iowa State University
item Freidberg, Iddo - Iowa State University
item Andorf, Carson

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/10/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Since its beginnings in 1991 to the present, the Maize Genetics and Genomics Database has been based on the concept of how to represent the maize genome and how that representation relates to the physical characteristics of the plant Zea mays and closely related species. The maize genome has gone from early map-based views (cytological, marker-based, and sequence-based) to full pseudomolecule genome assemblies. With the recent availability of multiple maize genome assemblies, it is inevitable that more robust pan-genome representations of the maize genome will be created in the near future. At the same time, both phenotype collection and analysis techniques have changed. The early curated phenotype data were derived from mutant stock collections hosted at the Maize Genetics Stock Center. More recently, bi-parental and diversity panel populations have allowed identification of important agronomic traits that are now linked to the genome. Currently, high-throughput phenotyping and the associated data management challenges are in the forefront. Here we present a brief history of phenotype curation in relation to the genome representations from pre-genomic maps to the present day pan-genome versions. Further, we address the current challenges and opportunities of both propagating existing data forward but also of data integration and visualization in an ever-changing landscape.