MZmine

Processing and annotation of direct infusion, LC-MS/MS, LC-IM-MS, imaging, and GC-MS datasets

Description

MZmine is a modular framework featuring various algorithms for different tasks. Lipid identification is performed by class specific fragmentation rules, or via spectra matching using most common database formats (e.g. json, msp, mgf and jdx), enabling the utilization of in-silico lipid libraries such as LipidBlast for LC-MS/MS data sets, as well as NIST and custom libraries. MZmine is also able to annotate lipids from GC-MS, LC-IM-MS, and imaging datasets. MZmine features a set of visualization tools, including Kendrick mass plots as a graphical screening tool for lipids. Dedicated exports of processed and annotated datasets for MetaboAnalyst and GNPS enable quick access to statistical analysis tools as well as feature-based molecular networking. MZmine includes a generic tool to create lipid subclass databases (can further be modified to search for reaction products e.g. to determine double bond positions by means of Paternò-Büchi reaction).

Technical Information

License:

GPL

GUI:

Yes

CLI:

Yes

Desktop client:

Yes

Web platform:

Input formats:

.raw(Waters),

.raw(Thermo),

.tdf(Bruker),

netCDF,

mzData,

mzXML,

imzML,

mzML

Output formats:

SIRIUS,

GNPS,

XML,

SQL,

mzTab,

MetaboAnalyst,

CSV

Platforms:

MacOS,

Linux,

Windows

Programming languages:

Java

Source code repository:

Yes

Download / Web-service link:

Yes

Documentation and user guide:

Mzmine user manual ,

Mzmine documentation

Tasks

4.2) Data dependent acquisition (DDA)

Shotgun MS/MS:

Yes

LC-MS:

Yes

Spectra matching:

Yes

Decision rules:

Yes

Requirements:

Spectra library and fragmentation rules (fragmentation rules are provided)

Algorithms:

Spectra matching based on cosine similarity, MS1 accuracy + MS2 rule-based assignment.

Scores:

Spectra matching cosine similarity, rule-based score: presence of fragments

Spectra annotation:

Yes

Support multiple adducts:

Yes

Correction for in source fragments:

Batch processing:

Yes

Other features:

Connected to online databases (KEGG, PubChem, HMDB, YMDB, LipidMaps, MassBank.eu, ChemSpider, MetaCyc),

Retention time mapping via Kendrick mass defect (KMD) plots

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

Peak processings:

Deisotoping,

Deconvolution,

Peak alignment,

Peak picking

Adjustable feature detection:

Yes

Quantification by:

Peak area

Normalization methods:

By assigning features as internal standards

Relative quantification:

Yes

Calibration curve:

6) Analysis and visualization of lipidomics data

Data pre-treatments:

Normalization (reference feature),

Various filtering options

Missing data handling:

Yes

Univariate statistical analysis methods:

ANOVA

Unsupervised multivariate statistical analysis methods:

PCA

Supervised multivariate statistical analysis methods:

Feature identification:

Clustering and correlation analysis methods:

K-means,

Farthest-first,

Expectation-maximization,

Hierarchical

Classification and feature selection:

Direct connection to identification results:

Yes