IDSL-IPA

IDSL-IPA processes untargeted LC/HRMS datasets to generate high-fidelity metabolomics data matrices and characterize organic chemical space for large-scale population studies.

Key Features:

• R package implementation: Implemented as an R package for processing LC/HRMS data.
• Carbon isotope pair identification: Identifies potential ^12C and ^13C ion pairs within individual mass spectra to distinguish isotopic variants.
• Chromatographic peak detection and characterization: Performs mass correction, peak smoothing, baseline development for local noise measurement, and peak quality assessment during chromatographic peak detection.
• Retention time correction and cross-referencing: Applies a dynamic retention index marker approach to correct retention times and cross-reference peaks across samples.
• Peak annotation using reference databases: Annotates peaks using reference database entries containing m/z and retention time information.
• Parallel computation for data processing acceleration: Uses parallel computation to accelerate peak detection and alignment for large datasets (reported handling of ~200–1600 samples).

Scientific Applications:

• Population-scale metabolomics: Produces reliable metabolomics data matrices suitable for population studies involving hundreds to thousands of samples.
• Exposomics: Isolates signals of carbon-containing compounds relevant to environmental exposure assessment.
• Biomarker and pathway discovery: Supports identification and characterization of compounds for metabolic pathway analysis and disease biomarker discovery.

Methodology:

Identification of ^12C/^13C ion pairs; mass correction; peak smoothing; baseline development for local noise measurement; chromatographic peak detection and peak quality assessment; retention time correction using dynamic retention index markers and cross-referencing peaks across samples; peak alignment; peak annotation using reference databases with m/z and retention time; and parallel computation for peak detection and alignment.