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EMBnet J. 2019;24. pii: e922. doi: 10.14806/ej.24.0.922. Epub 2019 May 22.

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

Author information

1
Laboratory of Genetics, Department of Biotechnology, School of Food, Biotechnology and Development, Agricultural University of Athens, Athens, Greece.
2
Computer Engineering and Informatics Department, School of Engineering, University of Patras, Patras, Greece.
3
Lab of Molecular Endocrinology, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
4
Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, United Kingdom.

Abstract

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.

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