ShinyTPs: Curating Transformation Products from Text Mining Results

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 toward 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 the 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 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.

Automated summary

Transformation product (TP) information is crucial for assessing the hazards of compounds, but the availability and usability of TP data are often limited. FAIRifying existing TP knowledge can improve data accessibility for identification workflows. ShinyTPs is an application that curates and visualizes text-mined chemical names to validate automatically extracted reactions. The application was successful in retrieving and adding newly curated reactions to the PubChem Transformations library, supporting TP identification in non-target analysis workflows.