Working with Ontologies

Modelling the bioinformatics tertiary analysis research process

Background

With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate.

Methods

To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities.

Results

The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools.

Conclusions

To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04310-5.

FOBI: an ontology to represent food intake data and associate it with metabolomic data

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/.

A Carcinogen-induced mouse model recapitulates the molecular alterations of human muscle invasive bladder cancer

The N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) mouse model is an attractive model system of muscle-invasive bladder cancer (MIBC) as it recapitulates the histology of human tumors in a background with intact immune system. However, it was unknown whether this carcinogen-induced model also mimicked human MIBC at the molecular and mutational level.

In our study, we analyzed gene expression and mutational landscape of the BBN model by next-generation sequencing followed by a bioinformatic comparison to human MIBC using data from The Cancer Genome Atlas and other repositories. BBN tumors showed overexpression of markers of basal cancer subtype, and had a high mutation burden with frequent Trp53 (80%), Kmt2d (70%), and Kmt2c (90%) mutations by exome sequencing, similar to human MIBC. Many variants corresponded to human cancer hotspot mutations, supporting their role as driver mutations. We extracted two novel mutational signatures from the BBN mouse genomes. The integrated analysis of mutation frequencies and signatures highlighted the contribution of aberrations to chromatin regulators and genetic instability in the BBN tumors.

Together, our study revealed several similarities between human MIBC and the BBN mouse model, providing a strong rationale for its use in molecular and drug discovery studies.