SLAW

SLAW processes large-scale untargeted liquid chromatography-mass spectrometry (LC-MS) data to detect, align, fill gaps, and consolidate MS² and isotopic information for metabolomics and lipidomics analyses.

Key Features:

• State-of-the-art peak-picking algorithms: Implements advanced peak-picking routines to enhance detection of peaks in complex LC-MS data for precise identification of metabolites and lipids.
• Automated parameter optimization routine: Performs automated optimization of algorithmic parameters to reduce parameter sensitivity and improve processing accuracy.
• Efficient sample alignment procedure: Aligns features across multiple samples to enable consistent comparison in large cohorts.
• Gap filling by data recursion: Recursively fills missing values to improve dataset continuity and completeness.
• Extraction of consolidated MS² and isotopic patterns: Extracts and consolidates MS² spectra and isotopic patterns across samples to support compound identification and quantification.
• Scalability and resource efficiency: Demonstrated processing of 2500 LC-MS files using 40% less memory and at six times the speed of an XCMS-based workflow.
• Improved feature recovery and identification rates: Extracted twice as many isotopic patterns and MS² spectra, with 60% yielding positive matches against a spectral library.

Scientific Applications:

• Metabolomics: Enables large-scale untargeted metabolite detection, alignment, and consolidation across extensive LC-MS datasets.
• Lipidomics: Facilitates identification and quantification of lipid species through consolidated MS² and isotopic pattern extraction.
• High-throughput LC-MS^n studies: Supports processing and comparative analysis of thousands of individual LC-MS^n runs for large-cohort studies.
• Applied and clinical research: Provides scalable workflows suitable for basic biological research, applied science, and clinical studies requiring reproducible feature detection.

Methodology:

Uses advanced peak-picking algorithms, automated parameter optimization, sample alignment, gap filling by data recursion, extraction/consolidation of MS² and isotopic patterns, and comparative benchmarking against openMS and XCMS.