MS-DIAL
Data processing pipeline for untargeted lipidomics
Description
MS-DIAL is an open source software for untargeted metabolomics and lipidomics data. The current version 4 series supports GC-MS, GC-MS/MS, LC-MS (Full MS), LC-DDA, LC-DIA (SWATH & all ion fragmentation), LC-Ion mobility (IM)-DDA, and LC-IM-DIA. Lipid annotation is based on retention time (optional), CCS (optional), m/z, and MS/MS (recommended) using a hybrid scoring system of classical spectral matching algorithm and defined fragmentation rules for each lipid subclass. The program provides an all-in-one solution from data import of MS raw data until lipidome table export (such as mztab-M) and statistical analyses.
Technical Information
Publications:
PMID:32541957
Training datasets:
Documentation and user guide:
Download / Web-service link:
Programming languages:
C#
Platforms:
Windows,
Linux,
MacOS
Output formats:
TSV
Input formats:
mzML,
netCDF,
abf,
ibf,
.d(Agilent),
.d(Bruker),
.wiff(SCIEX),
.wiff2(SCIEX),
.raw(Thermo),
.raw(Waters)
Web platform:
No
Desktop client:
Yes
CLI:
Yes
GUI:
Yes
License:
LGPL v3 (code) / CC-BY-SA (software)
Tasks
4.2)
Data dependent acquisition (DDA)
Other features:
Isotopic deconvolution,
Adduct/cluster/in-source ion grouping,
Alignment
Batch processing:
Yes
Correction for in source fragments:
Yes
Support multiple adducts:
Yes
Spectra annotation:
Yes
Scores:
Global scoring that takes into account retention time, mass accuracy, isotopic pattern and MS2 fragmentation
Algorithms:
MS1 accuracy, MS2 library matching, rule-based fragment assignment
Requirements:
User can load user-defined library (not obligatory, the internal library is included in the MS-DIAL environment)
Decision rules:
Yes
Based on lipid class specific fragmentation rules
Link to spectra matching:
Spectra matching:
Spectra libraries are included in the MS-DIAL environment, and the library is also available as MSP.
LC-MS:
Yes
Shotgun MS/MS:
No
4.1)
Full MS (HRAM LC-MS)
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