Lipostar 2.0 logo

Lipostar 2.0

Data processing pipeline for untargeted lipidomics

Description

Lipostar is a software developed by Molecular Discovery for LC-MS/MS-based lipidomics (DDA and DIA), which supports a large number of steps including lipid identification, quantification, statistical analysis, and biopathways analysis. Lipostar finds application either in untargeted, and semi-targeted lipidomics, including stable isotope labelling experiments. Within a Lipostar session, different modes of lipidomics analysis can be combined to increase the knowledge and obtain a more comprehensive analysis of lipid profiles. Lipid identification includes 1) a spectral matching approach, with the DB Manager module allowing to generate databases of fragmented lipids by applying fragmentation rules provided in the software or by importing experimental MS/MS data; 2) a high-throughput bottom-up approach, based on class-specific fragments recognition; 3) a high-throughput identification of oxidized species. Lipostar also includes unique features, such as the gap-filler to reduce the missing values and the trend analysis for global lipid profiling.

Technical Information

License:
Conditionally free (academic) / commercial license (commercial use) by request to Molecular Discovery (www.moldiscovery.com).
GUI:
Yes
CLI:
No
Desktop client:
Yes
Web platform:
No
Input formats:
.d(Agilent),
.d(Bruker),
.lcd(Shimadzu),
.raw(Thermo),
.raw(Waters),
.wiff(SCIEX)
Output formats:
Word,
CSV
Platforms:
Windows
Programming languages:
C++
Source code repository:
NA
Documentation and user guide:
Yes
Training datasets:
Yes
Publications:
PMID:28471643

Tasks

4.1) Full MS (HRAM LC-MS)
Shotgun MS/MS:
No
LC-MS:
Yes
Algorithms:
MS1 accuracy to a library (generated through the DB Manager) followed by isotopic intensity pattern check. Use of CCS values and RT or retention time ranges if available.
Spectra annotation:
Yes
Support multiple adducts:
Yes
Correction for in source fragments:
Yes
Lipid identification:
Matching to the Library (including isotopic pattern and, when defined, RT and CCS values)
Batch processing:
Yes
Other features:
See Lipid identification using DDA dataset (Data dependent acquisition 4.2).
4.2) Data dependent acquisition (DDA)
4.3) Data independent acquisition (DIA)
4.4) Tools considering ion mobility separation
4.5) Identification of oxidized lipids
5) Lipid quantification from untargeted lipidomics datasets (HRAM MS, DDA, DIA)
6) Analysis and visualization of lipidomics data
7.3) Pathway and network solutions