Abstract
Transformation product (TP) information is essential to accurately evaluate the hazards compounds pose to human health and the environment. However, information about TPs is often limited, and existing data is often not fully Findable, Accessible, Interoperable and Reusable (FAIR). FAIRifying existing TP knowledge is a relatively easy path towards improving access to data for identification workflows and for machine learning-based algorithms. ShinyTPs was developed to curate existing transformation information derived from text-mined data within the PubChem database. The application (available as an R package) visualizes the text-mined chemical names to facilitate user validation of the automatically extracted reactions. ShinyTPs was applied to a case study using 436 tentatively identified compounds to prioritize TP retrieval. This resulted in the extraction of 645 reactions (associated with 496 compounds), of which 319 reactions were not previously available in PubChem. The curated reactions were added to the PubChem Transformations library, which was used as a TP suspect list for identification of TPs using the open-source workflow patRoon. In total 72 compounds from the library were tentatively identified, 18% of which were curated using ShinyTPs, showing that the app can help support TP identification in non-target analysis workflows.
Supplementary materials
Title
Supplementary information tables
Description
Supplementary information Tables S1-S3. Compounds used for validation of the app and tentatively identified compounds in the wastewater sample.
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Supplementary weblinks
Title
GitLab repository for ShinyTPs
Description
GitLab repository for ShinyTPs containing all information related to the package.
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