An application ontology for extending interoperability for in-vivo bioassay protocols relevant to antimicrobial resistance (AMR) drug discovery.
To enhance the reproducibility and usability of bioassays in antimicrobial resistance (AMR) associated antibacterial drug discovery and development, there is an increasing need for standardisation of bioassay metadata into machine-readable formats. Such a standardisation process requires mapping bioassay data to standard ontologies and can be performed at the study result output and protocol levels. At the result output level, general ontologies such as the BioAssay Ontology (BAO) already exist, but antibacterial drug discovery and AMR-specific ontologies that can aid in the standardisation process at the protocol level are missing.
Hence, we propose an application-specific ontology that will enable researchers to convert unstructured bioassay protocol data into structured machine-readable formats thereby promoting their overall reusability.
The development of BPO is a first attempt to standardise and organise information within in-vivo AMR-related drug discovery bioassay protocol data in a structured manner. BPO will allow researchers to capture information regarding experimental details such as the type of mouse model, the bacterial strain, and the sex and growth phases of mouse and bacteria respectively from the protocol.
The building of this ontology is a way to promote use of sustainable and FAIR prinicples within experimental data records. The assay can plug-in your in-house workflows in either retrospective or prospective approach.
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Retrospective perspective - In this approach, you would train Natural Language Processing Model (NLP) to identify the entities relevant for replicability of the assay results for members outside the project or laboratory. The ontology can assist in training your NLP model or else can searve as starting point to understand what information should be captured for replication of reuslts.
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Prospective prespective - In this approach, you would enable formulation of templates for input of data within your system (e.g. electronic lab notebooks ELN). The bases for formulation of the template can be the underlying ontology.
IMI COMBINE is building pipelines to tackle both these fronts and will enable researches around the globe to leverage the underlying resources within their drug discovery pipeline.
The ROBOT tool was used to build this ontology and the structure of the ontology was formulated based on the Ontology Development Kit (ODK)
The ontology is available in Ontology Web Language (OWL) and Open Biomedical Ontologies (OBO)format.
The latest version of the ontology is 0.0.1 and can be found here.
BPO has 209 classes and 2,242 axioms and follows the BFO top-level ontology tree.
Since the current ontology is an application ontology, it makes use of various existing ontologies for its purpose. The ontologies used, and the number of terms extracted from them are shown below:
| Ontology Name | Ontology abbreviation | Number of terms | BFO compliant |
|---|---|---|---|
| NCBI taxonomy database | NCBITaxon | 120 | No |
| Phenotype And Trait Ontology | PATO | 7 | Yes |
| National Cancer Institute Thesaurus | NCIt | 13 | No |
| Vaccine Ontology | VO | 1 | No |
| Ontology for Biomedical Investigations | OBI | 4 | Yes |
| Ontology for MIRNA Target | OMIT | 1 | Yes |
| Microbial Conditions Ontology | MCO | 3 | Yes |
| Infectious Disease Ontology | IDO | 3 | Yes |
| Experimental Factor Ontology | EFO | 7 | Yes |
| Bioassay Protocol Ontology | BPO | 32 | Yes |
To get a brief overview of the project, check out the ESCMID 2022 Conference Poster
Since the ontology is not covering all domains of the AMR experimental data, there are going to be missing components. Nevertheless, we are always happy to hear from you what is missing and collaboratively work on adding new terms to the ontology. You can reach us via GitHub Issues or you can drop the developers an email.
| Person Name | Affiliation | Role |
|---|---|---|
| Yojana Gadiya | Fraunhofer Insititue for Translational and Medicine Pharamcology | Maintainer and Developer |
| Rakel Arrazuria | Paul-Ehrlich-Institut | Domain expert |
| Jon Ulf Hansen | Statens Serum Institut | Domain expert |
| Danielle Welter | Luxembourg Centre for Systems Biomedicine | Ontology expert |
| Fuqi Xu | European Molecular Biology Laboratory | Co-developer and ontology expert |
| Philippe Rocca-Serra | University of Oxford e-Research Centre | Ontology expert |
| Nick Juty | University of Manchester | Group Lead (FAIRplus) |
| Philip Gribbon | Fraunhofer Insititue for Translational and Medicine Pharamcology | Group Lead (COMBINE) |
This research was funded by the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking project Collaboration for prevention and treatment of MDR bacterial infections (COMBINE) IMI2 JU (grant agreement # 853967) and FAIRplus (grant agreement # 802750).
