Implementing Annotation Confidence Scoring in Untargeted Mass Spectrometry Workflows for Small Molecule Analysis

Untargeted small molecule analysis by high-resolution mass spectrometry is integral to environmental and biological research, enabling comprehensive characterization of complex samples. However, data interpretation and reporting remain challenging due to the complexity and high dimensionality of molecular features in untargeted data sets. Current data analysis platforms provide integrated tools for processing and annotation yet lack a standardized framework for assigning and reporting annotation confidence. 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 can be incorporated into a commercial data analysis software package and functions as a standalone postprocessing node, 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), and the consensus scoring algorithm is used to evaluate agreement among search nodes for greater accuracy. Validation using NIST SRM 1950 plasma samples demonstrated 100% scoring accuracy in negative mode and >99.5% in positive mode across both RPLC and HILIC separations. This tool enhances data reporting, improves transparency, and promotes consistency across studies, facilitating standardization and comparability of untargeted metabolomics results.