Abstract
Nutrition research can be conducted by using two complementary approaches: (i) traditional self-reporting methods or (ii) via metabolomics techniques to analyze food intake biomarkers in biofluids. However, the complexity and heterogeneity of these two very different types of data often hinder their analysis and integration. To manage this challenge, we have developed a novel ontology that describes food and their associated metabolite entities in a hierarchical way. This ontology uses a formal naming system, category definitions, properties and relations between both types of data. The ontology presented is called FOBI (Food-Biomarker Ontology) and it is composed of two interconnected sub-ontologies. One is a ’Food Ontology’ consisting of raw foods and ‘multi-component foods’ while the second is a ‘Biomarker Ontology’ containing food intake biomarkers classified by their chemical classes. These two sub-ontologies are conceptually independent but interconnected by different properties. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows (e.g. metabolite identification, experimental design) or the application of different enrichment analysis approaches to analyze nutrimetabolomic data. Availability: FOBI is freely available in both OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats at the project’s Github repository (https://github.com/pcastellanoescuder/FoodBiomarkerOntology) and FOBI visualization tool is available in https://polcastellano.shinyapps.io/FOBI_Visualization_Tool/.
Highlights
The growing emergence of high-throughput analytical techniques in the life sciences over the past three decades, such as next-generation DNA sequencing, proteomics, metabolomics and other high-throughput omics approaches, has created significant challenges in data management
FOBI is a freely available comprehensive ontology composed of two interconnected sub-ontologies including the ‘Food Ontology’ and the ‘Biomarker Ontology’
The Food Ontology was created on the basis of dietary data obtained from self-reported surveys for dietary assessment, including food frequency questionnaires (FFQ) and dietary recalls (DRs) [6]
Summary
The growing emergence of high-throughput analytical techniques in the life sciences over the past three decades, such as next-generation DNA sequencing, proteomics, metabolomics and other high-throughput omics approaches, has created significant challenges in data management. The heterogeneity of storage platforms, data formats and privacy requirements of some of them often hinders their widespread access and use In this vein, the creation of ontologies, defined as the ‘specification of a representational vocabulary for a shared domain of discourse— definitions of classes, relations, functions and other objects’ [1], is of vital importance to help analyze, annotate and homogenize these large and complex data sets [2, 3]. The creation of ontologies, defined as the ‘specification of a representational vocabulary for a shared domain of discourse— definitions of classes, relations, functions and other objects’ [1], is of vital importance to help analyze, annotate and homogenize these large and complex data sets [2, 3] This is a major issue within the ‘FAIR Guiding Principles for scientific data management and stewardship’ [4], which aim to improve the findability, accessibility, interoperability and reusability of data. With regard to traditional dietary assessment tools, it should be noted that subjective self-reports generate very complex textual data, containing types and quantities of foods and recipes in very diverse and heterogeneous formats that depend on the country/region, socio-demographic factors, etc
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