LipidQuant 1.0
LipidQuant 1.0 automates data processing for lipidomic quantitation workflows that integrate lipid class separation with hydrophilic interaction liquid chromatography (HILIC) or supercritical fluid chromatography (SFC) and high-resolution mass spectrometry for accurate lipid identification and quantitation.
Key Features:
- Automated Workflow: Automates processing from raw data to final reporting.
- Lipid Class Separation: Optimized for HILIC and SFC to enable co-ionization of lipid class internal standards with their respective analytes.
- High-Resolution Mass Spectrometry Integration: Integrates with high-resolution MS to provide detailed molecular information for lipid identification and quantitation.
- Comprehensive Data Processing Steps: Performs lipid identification using advanced algorithms, quantitation leveraging class-specific internal standards, isotopic correction to address interferences, and generation of comprehensive result reports.
Scientific Applications:
- Human serum lipidomics: Supports detailed lipidomic analysis of human serum samples.
- Clinical and biomedical research: Applicable to studies investigating disease mechanisms and biomarker discovery.
- Demonstrated application: Applied in a small cohort study of human serum samples.
Methodology:
Separation of lipid classes prior to mass spectrometric detection using HILIC or SFC to co-ionize internal standards with analytes, lipid identification via advanced algorithms, quantitation using internal standards, isotopic correction, and generation of result reports.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 2/20/2022
- Last Updated:
- 2/20/2022
Operations
Publications
Wolrab D, Cífková E, Čáň P, Lísa M, Peterka O, Chocholoušková M, Jirásko R, Holčapek M. LipidQuant 1.0: automated data processing in lipid class separation–mass spectrometry quantitative workflows. Bioinformatics. 2021;37(23):4591-4592. doi:10.1093/bioinformatics/btab644. PMID:34498026.