Biology subjects

Mendes de Farias, T.

Publications and source records attributed to Mendes de Farias, T..

4 recordsLinked to original sources

bioRxiv2025

METRIN-KG: A knowledge graph integrating plant metabolites, traits and biotic interactions

BackgroundIn recent years, biodiversity data management has emerged as a critical pillar in global conservation efforts. Today, the ability to efficiently collect, structure, and analyze biodiversity data is central to breakthroughs in conservation, drug development, disease monitoring, ecological forecasting, and agri-tech innovation. However, due to the vastness and heterogeneity of biodiversity data, it is often confined to databases for specific research areas in isolated formats and disconnected from other relevant resources. Crucial components of such data in kingdom Plantae comprise of metabolomes - the vast array of compounds produced by plants; traits - measurable characteristics of plants that influence their growth, survival, and reproduction, and that affect ecosystem processes; and biotic interactions - relationships of plants with other living organisms, affecting the ecosystem functions. ResultsIn this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph) a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions. ConclusionsThe proposed knowledge graph provides an interface to interactively search for data relating plant metabolomes, traits, and interactions. This, in turn, will facilitate development of research questions in life-sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.

bioinformatics↗

bioRxiv2023

Exploring Disease Model Mouse Using Knowledge Graphs: Combining Gene Expression, Orthology, and Disease Datasets

BackgroundThe RIKEN BRC develops and maintains the RIKEN BioResource MetaDatabase to help users explore appropriate target bioresources for their experiments and prepare precise and high-quality data infrastructures. The Swiss Institute of Bioinformatics develops two RDF datasets across multi species for the study of gene expression and orthology: Bgee and Orthologous MAtrix (OMA, an orthology database). MethodsThis study integrates the RIKEN BioResource knowledge graph with Resource Description Framework (RDF) datasets from Bgee, a gene expression database, the OMA, the DisGeNET, a human gene-disease association, Mouse Genome Informatics (MGI), UniProt, and four disease ontologies in the RIKEN BioResource MetaDatabase. Our aim is to explore which model organisms are most appropriate for specific medical science research applications, using SPARQL queries across the integrated datasets. More precisely, we attempt to explore disease-related genes, as well as anatomical parts where these genes are overexpressed and subsequently identify appropriate bioresource candidates available for specific disease research applications. ResultsWe illustrate the above through two use cases targeting either Alzheimers disease or melanoma. We identified two Alzheimers disease-related genes that were overexpressed in the prefrontal cortex (APP and APOE) and 21 RIKEN bioresources predicted to be relevant to Alzheimers disease research. Furthermore, executing a transitive search for the Uberon terms by using the Property Paths function, we identified two melanoma-related genes (HRAS and PTEN), and eight anatomical parts in which these genes were overexpressed, such as the "skin of limb" as an example. Finally, we compared the performance of the federated SPARQL query via the remote Bgee SPARQL endpoint with the performance of a centralized SPARQL query using the Bgee dataset as part of the RIKEN BioResource MetaDatabase. ConclusionsAs a result, we demonstrated that the performance of the federated approach degraded. We concluded that we improved the degradation of the query performance of the federated approach from the BioResource MetaDatabase to the SIB by refining the transferred data through the subquery and enhancing the server specifications, that is optimizing the triple store query evaluation.

bioinformatics↗

bioRxiv2020

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as "healthy" or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

bioinformatics↗

bioRxiv2019

Enabling Semantic Queries Across Federated Bioinformatics Databases

MotivationData integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.\n\nResultsWe introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: 1) Bgee, a gene expression relational database; 2) OMA, a Hierarchical Data Format 5 (HDF5) orthology data store, and 3) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialised RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.\n\nProject URLhttp://biosoda.expasy.org, https://github.com/biosoda/bioquery

bioinformatics↗