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Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

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Updated: Oct 14, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS.

Alexis Delabriere¹, Philipp Warmer¹, Vincenth Brennsteiner¹

¹Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.

Analytical Chemistry

|November 4, 2021

Summary

This summary is machine-generated.

We developed SLAW, a scalable workflow for processing large-scale untargeted metabolomics and lipidomics data. SLAW offers automated parameter optimization and efficient analysis, outperforming existing tools in feature detection and speed.

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Last Updated: Oct 14, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

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Published on: March 14, 2013

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Published on: May 27, 2014

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

Area of Science:

Metabolomics and Lipidomics
Bioinformatics and Computational Biology

Background:

Metabolomics research generates large datasets, but downstream analysis is challenging due to scalability and integration issues with existing tools.
Parameter optimization is critical but difficult for processing untargeted LC-MS data.

Purpose of the Study:

To present SLAW, a scalable and user-friendly workflow for processing untargeted liquid chromatography-mass spectrometry (LC-MS) data.
To address the challenges of large-scale data analysis and parameter optimization in metabolomics.

Main Methods:

SLAW integrates state-of-the-art peak-picking, automated parameter optimization, sample alignment, gap filling, and MS2/isotopic pattern extraction.
The workflow is designed for robust analysis of thousands of LC-MSn runs.

Main Results:

SLAW detected and aligned more reproducible features compared to openMS and XCMS workflows.
Analysis of 2500 LC-MS files showed SLAW used 40% less memory and was 6x faster than XCMS.
SLAW extracted twice as many isotopic patterns and MS2 spectra, leading to 60% positive library matches.

Conclusions:

SLAW provides a scalable, efficient, and robust solution for untargeted metabolomics and lipidomics data processing.
Automated parameter optimization and workflow integration enhance data analysis accuracy and speed.