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Implementation of Zebrafish Ontologies for Toxicology Screening

  • Jessica Legradi

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Toxicological evaluation of chemicals using early-life stage zebrafish (Danio rerio) involves the observation and recording of altered phenotypes. Substantial variability has been observed among researchers in phenotypes reported from similar studies, as well as a lack of consistent data annotation, indicating a need for both terminological and data harmonization. When examined from a data science perspective, many of these apparent differences can be parsed into the same or similar endpoints whose measurements differ only in time, methodology, or nomenclature. Ontological knowledge structures can be leveraged to integrate diverse data sets across terminologies, scales, and modalities. Building on this premise, the National Toxicology Program’s Systematic Evaluation of the Application of Zebrafish in Toxicology undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency. To accomplish this, zebrafish researchers were asked to assess images of zebrafish larvae for morphological malformations in two surveys. In the first survey, researchers were asked to annotate observed malformations using their own terminology. In the second survey, researchers were asked to annotate the images from a list of terms and definitions from the Zebrafish Phenotype Ontology. Analysis of the results suggested that the use of ontology terms increased consistency and decreased ambiguity, but a larger study is needed to confirm. We conclude that utilizing a common data standard will not only reduce the heterogeneity of reported terms but increases agreement and repeatability between different laboratories. Thus, we advocate for the development of a zebrafish phenotype atlas to help laboratories create interoperable, computable data.
Original languageEnglish
Article number817999
Pages (from-to)1-12
Number of pages12
JournalFrontiers in Toxicology. Computational Toxicology and Informatics
Volume4
Issue numberMarch
Early online date11 Mar 2022
DOIs
Publication statusPublished - Mar 2022

Funding

The Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS) supported this paper. Technical support was provided by ILS under NIEHS contract HHSN273201500010C.

FundersFunder number
ILSHHSN273201500010C
National Institute of Environmental Health Sciences

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