MaizeDIG: Maize Database of Images and Genomes

Authors: CHO, KYOUNG TAK - Iowa State University; Portwood, John; GARDINER, JACK - University Of Missouri; Harper, Elisabeth; LAWRENCE-DILL, CAROLYN - Iowa State University; FRIEDBERG, IDDO - Iowa State University; Andorf, Carson

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2019
Publication Date: 8/28/2019
Citation: Cho, K., Portwood II, J.L., Gardiner, J., Harper, E.C., Lawrence-Dill, C., Friedberg, I., Andorf, C.M. 2019. MaizeDIG: Maize Database of Images and Genomes. Frontiers in Plant Science. 10:1050. https://doi.org/10.3389/fpls.2019.01050.
DOI: https://doi.org/10.3389/fpls.2019.01050

Interpretive Summary: An organism can be described by both its observable characteristics (phenotypes) and the underlying genes and genomic data (genotype) that cause the phenotype. There has been tremendous growth in data for both genomic data and phenotype imaging. This growth has been seen in the maize research community with sequencing of thousands of maize accessions and the availability of large-scale phenotype data. A challenge at MaizeGDB, the model organism database for maize, is to be able to build connections between the phenotype and genotype data sets. A GMOD project that consists of a web-based software package that allows annotation of the genotypic-phenotypic relationships is called BioDIG (Biological database of images and genomes). To integrate the phenotype images and the genomics information at MaizeGDB, we implemented and updated a maize-based version of BioDIG called MaizeDIG. MaizeDIG is enhanced to handle multiple genomes and integrated with genome browsers to make tracks showing mutant phenotypes images within their genomic context. MaizeDIG allows for custom tagging of images to highlight regions related to the phenotypes and to curate and search by gene model, gene symbol, gene name, and allele. MaizeDIG is preloaded with 2,721 mutant phenotype images that are available on ten genome browsers.

Technical Abstract: Background: An organism can be described by its visual and/or biochemical features (phenotypes) and the genes and genomic information (genotypes) that cause these phenotypes. For many decades, researchers have tried to find relationships between genotypes and phenotypes and great strides have been made. However, improved methods and tools for discovering and visualizing these phenotypic relationships are still needed. The maize genetics and genomic database (MaizeGDB, www.maizegdb.org) provides an array of useful resources for genomic data and makes available a large collection of diverse data types related to phenotypes including thousands of images related to mutant phenotypes in Zea mays (maize). To integrate those mutant phenotype images with genomics information, we have implemented the web-based software package BioDIG (Biological database of images and genomes). Findings: We have developed a genotypic-phenotypic database for maize based on the BioDIG software package called MaizeDIG. Additionally, the MaizeDIG database and tool set have been enhanced to handle multiple reference genome assemblies simultaneously. MaizeDIG has been seamlessly integrated with the MaizeGDB Genome Browser to accommodate custom tracks showing images of mutant phenotypes in their genomic context. MaizeDIG also allows for detailed, custom tagging of images to highlight those regions within the image that are specifically related to the phenotype. This is accomplished through an interface allowing users to create links from images to genomic coordinates and to curate and search images by gene model ID, gene symbol, and gene name. Conclusions: We have created an easy to use and easily extensible web-based resource called MaizeDIG. MaizeDIG is preloaded with 2,396 images that is available for ten different genome browsers at MaizeGDB. Approximately 90 maize classical gene images have been manually annotated to clarify and highlight phenotypes. MaizeDIG is available at (http://maizedig.maizegdb.org/).