MaizeGDB 2018: the maize multi-genome genetics and genomics database.

Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity.

The physical and genetic framework of the maize B73 genome.

Maize is a major cereal crop and an important model system for basic biological research. Knowledge gained from maize research can also be used to genetically improve its grass relatives such as sorghum, wheat, and rice. The primary objective of the Maize Genome Sequencing Consortium (MGSC) was to generate a reference genome sequence that was integrated with both the physical and genetic maps.

MaizeDB - a functional genomics perspective.

MaizeDB (http://www.agron.missouri.

Searching and mining visually observed phenotypes of maize mutants.

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects.

Physical and genetic structure of the maize genome reflects its complex evolutionary history.

Maize (Zea mays L.) is one of the most important cereal crops and a model for the study of genetics, evolution, and domestication. To better understand maize genome organization and to build a framework for genome sequencing, we constructed a sequence-ready fingerprinted contig-based physical map that covers 93.

Whither biological database research?

We consider how the landscape of biological databases may evolve in the future, and what research is needed to realize this evolution. We suggest today's dispersal of diverse resources will only increase as the number and size of those resources, driving the need for semantic interoperability even more strongly. Because the complexity of the questions biologists want answered automatically continues to rapidly escalate, we will need to draw upon high-performance computing resources such as the GRID to process complex queries.