Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Matentzoglu, Nicolas; Goutte-Gattat, Damien; Tan, Shawn Zheng Kai; Balhoff, James P.; Carbon, Seth; Caron, Anita R.; Duncan, William D.; Flack, Joe E; Haendel, Melissa; Harris, Nomi L.; Hogan, William R.; Hoyt, Charles Tapley; Jackson, Rebecca; Kim, HyeongSik; Kır, Hüseyin; Larralde, Martin; McMurry, Julie A.; Overton, James A.; Peters, Bjoern; Pilgrim, Clare; Stefancsik, Ray; Robb, Sofia; Toro, Sabrina; Vasilevsky, Nicole; Walls, Ramona; Mungall, Christopher J.; Osumi-Sutherland, David

doi:10.1093/database/baac087

Cited by 37 publications

(30 citation statements)

References 27 publications

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“…1 & 3 for example implementation). The BDSO is built using the Ontology Development Kit 20 and uses standard ontology term templating systems 21 , 22 to generate labels, definitions and synonyms for BDSO terms and to add CL classifications and relationships (more strictly, existential restrictions in Web Ontology Language (OWL)) recording location (using Uberon terms 17 ), species (using NCBI taxonomy terms 16 ), markers, projection patterns and morphologies (see Fig. 4 for examples).…”

Section: Resultsmentioning

confidence: 99%

“…We updated the Provisional Cell Ontology to follow OBO Foundry standards by using a pipeline based on the ontology development kit 20 . Earlier, manually generated releases of PCL shared terms with the version described here, but used non-standard IDs and schema.…”

Section: Methodsmentioning

confidence: 99%

“…The BDSO is generated using a dedicated ontology build pipeline, built as an extension to the Ontology Development Kit 20 , but released as a component of the PCL, with all terms having PCL IDs. Previous releases of PCL 3 , 11 , 30 represented some of the same cell types as the current release but used a different, less formal schema and a different ID system 3 , 11 , 30 .…”

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confidence: 99%

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Brain Data Standards - A method for building data-driven cell-type ontologies

et al. 2023

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Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Brain Data Standards - A method for building data-driven cell-type ontologies

et al. 2023

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“…Further, the predicted mappings include confidence assessments and additional provenance about how they were generated. These are collated into a single SSSOM document that fully represents all provenance, can be used to generate additional exports (e.g., JSON, RDF), and can be readily consumed by curation workflows such as those in the Ontology Development Kit (ODK) (Matentzoglu et al ., 2022b) for developing ontologies. These artifacts are re-generated by the continuous integration workflow triggered on all data changes in the repository.…”

Section: Resultsmentioning

confidence: 99%

Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings

Hoyt

Gyori

2022

Preprint

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Biomedical identifier resources (ontologies, taxonomies, controlled vocabularies) commonly overlap in scope and contain equivalent entries under different identifiers. Maintaining mappings for these relationships is crucial for interoperability and the integration of data and knowledge. However, there are substantial gaps in available mappings motivating their semi-automated curation. Biomappings implements a curation cycle workflow for missing mappings which combines automated prediction with human-in-the-loop curation. It supports multiple prediction approaches and provides a web-based user interface for reviewing predicted mappings for correctness, combined with automated consistency checking. Predicted and curated mappings are made available in public, version controlled resource files on GitHub. Biomappings currently makes available 8,560 curated mappings, and 41,178 predicted ones, providing previously missing mappings between widely used resources covering small molecules, cell lines, diseases and other concepts. We demonstrate the value of Biomappings on case studies involving predicting and curating missing mappings among cancer cell lines as well as small molecules tested in clinical trials. We also present how previously missing mappings curated using Biomappings were contributed back to multiple widely used community ontologies. The data and code are available under the CC0 and MIT licenses at https://github.com/biopragmatics/biomappings.

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“…VFB is built around the Drosophila Anatomy Ontology (DAO) ( Costa et al, 2013 ), a manually curated, query-able classification of Drosophila anatomical structures and cell types expressed in Web Ontology Language (OWL) ( World Wide Web Consortium, 2012 ). DAO is built using community standards ( Jackson et al, 2021 ) and tooling ( Matentzoglu et al, 2022 ) for sustainable, scalable ontology development. Neuroanatomy is represented in DAO using a standard schema that supports recording neuronal location, connectivity, lineage and function and incorporates basic spatial reasoning ( Osumi-Sutherland et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Virtual Fly Brain—An interactive atlas of the Drosophila nervous system

Court

Costa²,

Pilgrim³

et al. 2023

Front. Physiol.

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As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

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Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Cited by 37 publications

References 27 publications

Brain Data Standards - A method for building data-driven cell-type ontologies

Brain Data Standards - A method for building data-driven cell-type ontologies

Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings

Virtual Fly Brain—An interactive atlas of the Drosophila nervous system

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