Location: Plant Genetics ResearchTitle: Maize databases
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 2/4/2014
Publication Date: 8/5/2014
Citation: Schaeffer, M.L., Sen, T.Z., Lawrence, C.J. 2014. Maize databases. In: Wusirika, R., Bohn, M., Lai, J., Kole, C., editors. Genetics, Genomics and Breeding of Maize. Boca Raton, FL: CRC Press. p. 215-235.
Technical Abstract: This chapter is a succinct overview of maize data held in the species-specific database MaizeGDB (the Maize Genomics and Genetics Database), and selected multi-species data repositories, such as Gramene/Ensembl Plants, Phytozome, UniProt and the National Center for Biotechnology Information (NCBI), and the types of tools researchers find useful, many at sites other than MaizeGDB. Bioinformatics strategies to access data at different resources seamlessly are described. The chapter is organized by datatype and describes access to public data about: (1) B73 reference genome assembly and its documentation; (2)maize sequence diversity; (3)sequence-indexed mutant collections; (4) the transcriptome, defined as quantitative mRNA levels, both baseline, over the maize plant growth cycle, and as determined under different environments; (5) the proteome, defined as quantitative protein levels for each gene model; (6) gene models associated with steps in metabolic pathways, largely computed from experimentally confirmed data in other species such as Arabidopsis ; and (7) protein structures. Specific examples depict how researchers may locate candidate genes for the agronomic trait, ‘kernel oil quality’. One way, the Locus Pair Lookup Tools at MaizeGDB, displays candidate genes defined by flanking markers for a quantitative trait locus (QTL). Two other ways include (1) heat maps of data from gene expression studies, for example tissues selected at different stages of kernel development and (2) paints chromosomes painted with locations of candidate genes for individual metabolic pathways. The need for literature curation to make the metabolism tools more robust is discussed, along with textming efforts to make this a realistic process for databases such as MaizeGDB.