LipidMSTool

Área:
Oncología y Hematología
Grupo:
Unidad de Biomarcadores y Medicina de Precisión (UBYMP)
Tipo:
Software

In the last years, lipidomics has emerged as a rapidly evolving tool in many fields of science. In our laboratory, we are particularly interested in unravelling the complex role of the wide range of lipids in the pathogenesis of disease (e.g. cancer). To this end, we are deeply committed in the development of workflows and tools focused on improving lipidome analysis and lipid annotation when liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) approaches are used. In this regard, we have developed the following tools.


LipidMS

LipidMS is an R-package aimed to confidentially identify complex lipids in untargeted LC-MS DIA and DDA data analysis. The lipid identification is based on a set of fragmentation rules and a coelution scores between parent and fragment ions present within predefined retention time windows1. A recent update of LipidMS allowed batch data processing from a number of mass spectrometry vendors (i.e., Thermo, Agilent). Currently, the data analysis workflow is also available as a web-based tool with an user-friendly interface.

New features included in LipidMS v3.0:

  • Batch processing: peak-picking, grouping and alignment wrapped in batchdataProcessing(). Lipid annotation for msbatch objects simplified with annotatemsbatch().
  • New lipid classes: plasmanyl and plasmenyl PC and PE, acylceramides and ceramides phosphate.
  • GUI through shiny app running LipidMSapp().
  • Improved graphical outputs for lipid annotation.

LipidMS is intended to be used for research purposes only, without any medical objective.

Click here to access LipidMS. 

References

  1. Alcoriza-Balaguer MI, García-Cañaveras JC, López A, Conde I, Juan O, Carretero J, Lahoz A. LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics. Anal Chem. 2019 Jan 2;91(1):836-845. doi: 10.1021/acs.analchem.8b03409. Epub 2018 Dec 13. PMID: 30500173.