Gene-Centric Knowledgebases for Identification of Agronomic Trait Lead Candidates

To meet the growing global demand for agricultural crops for food, fuel, and fiber it is crucial to understand the molecular components controlling crop productivity. In recent years, decreasing costs for whole genome sequencing and advances in high throughput data generation have resulted in a deluge of large, high-dimensional datasets for important agricultural crops such as maize. As a result, it has become increasing difficult to maintain, integrate, and efficiently leverage this escalating amount of data. We have found that the integration of heterogeneous and previously decentralized datasets into knowledgebases utilizing a gene-centric data model has significantly improved our data organization, knowledge capture, and data mining capabilities. In this talk, I will describe two approaches we have taken to integrate data into gene-centric knowledgebases: the development of single crop/single trait-focused knowledgebases using BioXM and the development of a trait-agnostic, multi-crop knowledgebase using MongoDB and Elastic Search. Additionally, I will discuss how the implementation of these knowledgebases has increased our ability to quickly leverage numerous datasets to identify gene candidates potentially controlling important agronomic traits.