Abstract
SummaryAs one of the most extensively cultivated crops, maize (Zea mays L.) has been extensively studied by researchers and breeders for over a century. With advances in high-throughput detection of various omics data, a wealth of multi-dimensional and multi-omics information has been accumulated for maize and its wild relative, teosinte. Integration of this information has the potential to accelerate genetic research and generate improvements in maize agronomic traits. To this end, we constructed ZEAMAP, a comprehensive database incorporating multiple reference genomes, annotations, comparative genomics, transcriptomes, open chromatin regions, chromatin interactions, high-quality genetic variants, phenotypes, metabolomics, genetic maps, genetic mapping loci, population structures, and populational DNA methylation signals within maize inbred lines. ZEAMAP is user friendly, with the ability to interactively integrate, visualize, and cross-reference multiple different omics datasets.
Highlights
Maize (Zea mays L.) is one of the most important crops for food, feed, and fuel and is a model species for genetic and genomic researches
With advances in high-throughput detection of various omics data, a wealth of multidimensional and multi-omics information has been accumulated for maize and its wild relative, teosinte
We have developed ZEAMAP, a multi-omics database for maize research and breeding, which integrates omics data generated from 507 elite inbred lines (Yang et al, 2011) and 183 teosinte accessions
Summary
Maize (Zea mays L.) is one of the most important crops for food, feed, and fuel and is a model species for genetic and genomic researches. As the cost of sequencing has been decreased and new omics technologies have arisen, there has been an explosive growth in the amount of biological information available for maize. The previous two-dimensional genome has recently been resolved in three dimensions with the mapping of open chromatin and the identification of chromatin interactions based on ChiA-PET and Hi-C technologies (Peng et al, 2019; Rodgers-Melnick et al, 2016). There are many different applications for these new datasets, including gene cloning and the study of regulatory networks. These new and comprehensive datasets provide valuable resources for maize research and have the potential to completely revolutionize breeding (Wallace et al, 2018)
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