JPA

JPA extracts and annotates metabolic features from LC-MS data to improve detection and identification of low‑abundance compounds in untargeted metabolomics and exposome studies.

Key Features:

• Enhanced Feature Extraction: Integrates conventional peak picking algorithms with advanced strategies to identify low-intensity signals, poorly shaped chromatographic peaks, and features that do not fit standard parameter settings.
• Tandem Mass Spectra Utilization: Leverages tandem mass spectra (MS²) to recognize and recover features with suboptimal peak shapes.
• Targeted List Integration: Accepts user-defined targeted lists to focus extraction on specified features of interest.
• Improved Detection Limits: Lowers limits of detection (LOD), demonstrated on serially diluted metabolite standard mixtures analyzed in HILIC(-) and RP(+) modes with LODs reported thousands of times lower than conventional methods.

Scientific Applications:

• Global Metabolomics: Validated on serially diluted urine samples, rescuing an average of 25% of metabolic features missed by traditional peak picking and assessing chromatographic peak shapes, analytical accuracy, and precision.
• Exposome Research: Detected a mixture of 250 drugs and 255 pesticides at environmentally relevant concentrations with an average detection rate 2.3‑fold higher than methods relying solely on peak picking.

Methodology:

Integrates conventional peak picking algorithms with advanced signal-detection strategies, leverages MS² information, accepts user-defined targeted lists, and includes evaluation of chromatographic peak shapes along with measures of analytical accuracy and precision.