Annotation Confidence Scoring

Scripting node for Compound Discoverer

Communicating varying levels of confidence in untargeted data sets continues to pose challenges without an automated ranking system. To address this, a custom scripting node was developed that assigns annotation confidence levels based on the widely adopted Schymanski et al. scoring scheme. While implemented here for metabolomics, the scoring approach is broadly applicable to other untargeted small molecule workflows. The script functions as a standalone postprocessing node for Compound Discoverer, expanding the original five-level system with four new sublevels (levels 3a/3b and 4a/4b) to improve specificity and distinguish cases that fall between established categories. Annotation confidence is assessed using available information from all compound identification workflow nodes (e.g., Predicted Composition, mzVault, mzCloud, ChemSpider). This tool enhances data reporting, improves transparency, and promotes consistency across studies, facilitating standardization and comparability of untargeted metabolomics results.

Please see https://pubs.acs.org/doi/10.1021/acs.analchem.5c03229 for more details.