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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #432076
Research Project: MaizeGDB - Database and Computational Resources for Maize Genetics, Genomics, and Breeding Research

Location: Corn Insects and Crop Genetics Research

Title: Using the MaizeGDB genome browser to evaluate gene model annotation quality and gain functional insights

Author
item Portwood Ii, John
item TIBBS-CORTES, LAURA - Oak Ridge Institute For Science And Education (ORISE)
item HALEY, OLIVIA - Oak Ridge Institute For Science And Education (ORISE)
item Cannon, Ethalinda
item GARDINER, JACK - University Of Missouri
item Woodhouse, Margaret
item Andorf, Carson

Submitted to: Maize Genetics Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 2/3/2026
Publication Date: 2/28/2026
Citation: Portwood II, J.L., Tibbs-Cortes, L., Haley, O., Cannon, E.K., Gardiner, J., Woodhouse, M.H., Andorf, C.M. 2026. Using the MaizeGDB genome browser to evaluate gene model annotation quality and gain functional insights. Maize Genetics Conference Abstracts.

Interpretive Summary:

Technical Abstract: MaizeGDB's genome browsers provide a suite of tools, data, and resources to enable users to evaluate gene model annotation quality and gain functional insights into maize genomics. MaizeGDB now has a set of Syntenome tracks that offer whole-genome alignments with other grasses and lifted gene models, enable comparative analyses that support gene model validation, and highlight conserved and diverged regions. These tracks include unmethylated regions, aligned protein fragments from the Maize Peptide Atlas, and confidence scores from protein structure predictions. Along with over 300 gene expression data, these datasets support a robust framework for both inter-species and cross-species comparisons. Additionally, protein structure alignments from AlphaFold and ESMFold, alongside full proteome alignments from over 20 species via UniProt and Phytozome, lend further validation and functional context. Alternative annotations from NCBI, Helixer, and Mikado offer alternative or confirmatory evidence, while TSS start site predictions from CAGE data refine transcriptional start site annotations. Functional insights are further enriched by curated annotations, including UniProt descriptions, Gene Ontology terms, AI-based predictions, and transcription factor annotations from Grassius. Pangenome and pan-gene annotations from the NAM founder genomes and other high-quality maize assemblies enable users to explore structural and functional diversity across maize lineages. Over 600 epigenetic and DNA-binding datasets, such as transcription factor binding sites, open chromatin regions, and histone modifications, add additional layers of regulatory context. The genome browser also integrates tools for visualizing SNPs, INDELs, and large-scale variations, alongside forward genetics resources like UniformMu, BonnMu, and Ac/Ds insertions. Tight integration with gene model pages, BLAST tools, SNPVersity (a variant viewer), and PanEffect (a variant effect viewer) ensures seamless navigation and analysis. Together, these resources allow users to critically evaluate gene models, uncover functional insights, and advance maize genetics and genomics research. By providing researchers and breeders with genome-browser evidence to validate maize gene models and interpret function across public datasets, MaizeGDB advances USDA’s priorities by improving the accuracy of targets used to deliver higher-yielding, pest- and disease-resistant germplasm that boost producer profitability, strengthen resilience to pests and diseases, and improve human health.