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

License:

LGPL v3 (code) / CC-BY-SA (software)

GUI:

Yes

CLI:

Yes

Desktop client:

Yes

Web platform:

Input formats:

.raw(Waters),

.raw(Thermo),

.wiff2(SCIEX),

.wiff(SCIEX),

.d(Bruker),

.d(Agilent),

ibf,

abf,

netCDF,

mzML

Output formats:

TSV

Platforms:

MacOS,

Linux,

Windows

Programming languages:

Download / Web-service link:

Yes

Documentation and user guide:

Metabolomics ,

Mtbinfo

Training datasets:

Yes

Publications:

PMID:32541957

Tasks

4.3) Data independent acquisition (DIA)

Input formats:

.raw(Waters),

.raw(Thermo),

.wiff2(SCIEX),

.wiff(SCIEX),

.d(Bruker),

.d(Agilent),

abf

AIF like:

Yes

SWATH like:

Yes

MS-MS2 Deconvolution algorithm:

MSDec algorithm, see,

https://www.nature.com/articles/nmeth.3393

Additional requirements:

The dictionary file including the precursor isolation window information is needed

Other features:

See Lipid identification using DDA dataset (Data dependent acquisition 4.2)

4.1) Full MS (HRAM LC-MS)

Shotgun MS/MS:

LC-MS:

Yes

Algorithms:

MS1 accuracy matching to a library (the MS-DIAL internal database or user-defined library)

Support multiple adducts:

Yes

Correction for in source fragments:

Yes

Lipid identification:

Build-in matching to the MS-DIAL internal database or user-defined library.

Batch processing:

Yes

Other features:

See Lipid identification using DDA dataset (Data dependent acquisition 4.2)

4.2) Data dependent acquisition (DDA)

Shotgun MS/MS:

LC-MS:

Yes

Spectra matching:

Spectra libraries are included in the MS-DIAL environment, and the library is also available as MSP.

Link to spectra matching:

Yes

Decision rules:

Yes

Requirements:

User can load user-defined library (not obligatory, the internal library is included in the MS-DIAL environment)

Algorithms:

MS1 accuracy, MS2 library matching, rule-based fragment assignment

Scores:

Global scoring that takes into account retention time, mass accuracy, isotopic pattern and MS2 fragmentation

Spectra annotation:

Yes

Support multiple adducts:

Yes

Correction for in source fragments:

Yes

Batch processing:

Yes

Other features:

Alignment,

Adduct/cluster/in-source ion grouping,

Isotopic deconvolution

4.4) Tools considering ion mobility separation

Accepted data formats:

.raw(Waters),

.d(Bruker),

.d(Agilent),

IBF

Use CSS for identification:

The CCS value is used for scoring and filtering candidates, the score is calculated by the equation following gaussian distribution, see,

https://www.nature.com/articles/nmeth.3393

Annotate CSS values to lipids:

Yes

Other features:

See Lipid identification using DDA dataset (Data dependent acquisition 4.2)

4.5) Identification of oxidized lipids

Lipid coverage:

GPL

Spectra matching:

Yes

Decision rules:

Yes

Requirements:

Oxidized lipids spectra library (build-in, and it is also available as MSP format) see,

http://prime.psc.riken.jp/compms/msdial/main.html

Algorithms:

MS1 accuracy, MS2 library matching

Scores:

Global scoring that takes into account mass accuracy, isotopic patter and MS2 fragmentation

Spectra annotation:

Yes

Other features:

Isotopic deconvolution, adduct/cluster/in-source ion grouping.

5) Lipid quantification from untargeted lipidomics datasets (HRAM MS, DDA, DIA)

Peak processings:

Deconvolution,

Peak alignment,

Peak picking

Adjustable feature detection:

Yes

Quantification by:

Peak area

Normalization methods:

Total ion count based (for relative intensity-based sample comparison),

Quality control sample’s profiles based (for LOWESS method),

Internal standard based (for relative intensity-based sample comparison)

Relative quantification:

Yes

Semi absolute quantification using internal standards:

EquiSPLASH,

SPLASH Lipidomics II,

SPLASH Lipidomics I

Calibration curve:

Yes

6) Analysis and visualization of lipidomics data

Data pre-treatments:

Scaling,

Total ion count based (for relative intensity-based sample comparison),

Quality control sample’s profiles based (for LOWESS method),

An internal standard based (for relative intensity-based sample comparison),

Filtering by blank’s peaks

Missing data handling:

Yes

Univariate statistical analysis methods:

Fold-change analysis,

ANOVA

Unsupervised multivariate statistical analysis methods:

PCA

Supervised multivariate statistical analysis methods:

O-PLS-DA,

O-PLS,

PLS-DA,

PLS

Feature identification:

Clustering and correlation analysis methods:

HCA

Classification and feature selection:

OPLS-DA,

PLS-DA

Direct connection to identification results:

Yes