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In the big data era, conventional bioinformatics seems to fail in managing the full extent of the available genomic information. The current study is focused on olive tree species and the collection and analysis of genetic and genomic data, which are fragmented in various depositories. Extra virgin olive oil is classified as a medical food, due to nutraceutical benefits and its protective properties against cancer, cardiovascular diseases, age-related diseases, neurodegenerative disorders, and many other diseases. Extensive studies have reported the benefits of olive oil on human health. However, available data at the nucleotide sequence level are highly unstructured. Towards this aim, we describe an in-silico approach that combines methods from data mining and machine learning pipelines to ontology classification and semantic annotation. Fusing and analysing all available olive tree data is a step of uttermost importance in classifying and characterising the various cultivars, towards a comprehensive approach under the context of food safety and public health.

A genomic data mining pipeline for 15 species of the genus Olea