Details

Autor(en) / Beteiligte

• Stefancsik, Ray
• Balhoff, James P.
• Balk, Meghan A.
• Ball, Robyn L.
• Bello, Susan M.
• Caron, Anita R.
• Chesler, Elissa J.
• de Souza, Vinicius
• Gehrke, Sarah
• Haendel, Melissa
• Harris, Laura W.
• Harris, Nomi L.
• Ibrahim, Arwa
• Koehler, Sebastian
• Matentzoglu, Nicolas
• McMurry, Julie A.
• Mungall, Christopher J.
• Munoz-Torres, Monica C.
• Putman, Tim
• Robinson, Peter
• Smedley, Damian
• Sollis, Elliot
• Thessen, Anne E.
• Vasilevsky, Nicole
• Walton, David O.
• Osumi-Sutherland, David

Titel

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

Ist Teil von

• Mammalian genome, 2023-09, Vol.34 (3), p.364-378

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.