The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Abstract

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis

“…Broad search terms for disease-related traits, although rapidly computationally reproducible and readily applicable to whole vocabularies and databases, may result in false-positive search results, and manual filtering, although labor intensive, may improve the precision of global associations of variants to disease. Use of specific mappings of traits to disease developed in efforts such as the Ontology of Biological Attributes ( Stefancsik et al 2023 ) will further refine the precision of disease-relevant queries of mouse trait data. Overall, we observed a somewhat low overlap of genes implicated in both human and mice, particularly for multiple substance use.…”

Automated Annotation of Disease Subtypes

“…"Total Source Diseases'', indicates the total number of diseases (regardless of subtype) from a source. a Sources are described in [12], [58][59][60][61][62].…”

Mouse phenome database: curated data repository with interactive multi-population and multi-trait analyses

“…Data are curated and annotated with community standard ontologies such as Mammalian Phenotype (MP) (Smith and Eppig 2012 ), Vertebrate Trait (VT) (Park et al 2013 ), and Adult Mouse Anatomy (MA) (Hayamizu et al 2005 ) ontologies. These attributes are related to human disease through an ongoing effort to integrate human and mouse phenotype data via dominant ontologies (Human Phenotype Ontology and Mammalian Phenotype ontology) through the Mouse-Human Ontology Mapping Initiative (Stefancsik et al 2023 ). These mappings facilitate data selection and analyses that aggregate mouse data by human disease annotations.…”