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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

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

Tasks

4.3) Data independent acquisition (DIA)
Other features:
See Lipid identification using DDA dataset (Data dependent acquisition 4.2)
Additional requirements:
No
MS-MS2 Deconvolution algorithm:
Yes
SWATH like:
Yes
AIF like:
Yes
DIA type and input formats:
.wiff(SCIEX),
.raw(Waters),
.raw(Thermo),
.lcd(Shimadzu),
.d(Bruker),
.d(Agilent)
4.1) Full MS (HRAM LC-MS)
4.2) Data dependent acquisition (DDA)
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