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.

PMID: 34498026
Funding: - Czech Science Foundation: 21-20238S

Documentation