Semantic Integration of Information Layers in Plant Sciences with the BioXMTM Knowledge Management Environment

Current demands in plant science to provide crops with higher yield and nutritional value as well as varieties adapted to different environmental conditions are continuously increasing. A large amount of knowledge about plant physiology is available in the literature and is recently being complemented with Systems Biology approaches on data generation. However, for the most part, knowledge and data remain in isolated databases. Integrating different types of information like diversity, geographical, phenotypic, genetic, regulatory, metabolic as well as data generated by high-throughput methodologies is needed for better understanding of plant systems. The BioXM^TM Knowledge Management Environment semantically integrates different layers of knowledge, information and data sources. The system is configuration-based, allowing designing and adapting a knowledge model, import or federate to existing information and query the network of knowledge and report results. The system is enriched by integrated data sets (EntrezGene, PubChem, Literature) and ontologies (NCBI-Taxonomy, GO, EnvO, PO and Trait-ontology). We have taken datasets from the literature to illustrate what BioXM^TM offers.  Areas of plant research including exploration of natural genetic variation, field trials for morphological, biochemical and molecular characterizations in different geographical locations as well as genetic mapping experiments to identify QTL associated with disease resistance are presented. These examples show how the BioXM^TM Knowledge Management Environment can help researchers to integrate experimental data with other sources of information, query, navigate the knowledge-network and generate knowledge-based hypotheses. It opens avenues for understanding aspects of plant biology like plant responses to environmental stress, plant-pathogen interactions and assisting trait improvement.