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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

Source code repository:
Programming languages:
Java
Platforms:
Windows,
Linux,
MacOS
Output formats:
CSV,
MetaboAnalyst,
mzTab,
SQL,
XML,
GNPS,
SIRIUS
Input formats:
mzML,
imzML,
mzXML,
mzData,
netCDF,
.tdf(Bruker),
.raw(Thermo),
.raw(Waters)
Web platform:
No
Desktop client:
Yes
CLI:
Yes
GUI:
Yes
License:
GPL

Tasks

4.2) Data dependent acquisition (DDA)
Other features:
Retention time mapping via Kendrick mass defect (KMD) plots,
Connected to online databases (KEGG, PubChem, HMDB, YMDB, LipidMaps, MassBank.eu, ChemSpider, MetaCyc)
Batch processing:
Yes
Correction for in source fragments:
No
Support multiple adducts:
Yes
Spectra annotation:
Yes
Scores:
Spectra matching cosine similarity, rule-based score: presence of fragments
Algorithms:
Spectra matching based on cosine similarity, MS1 accuracy + MS2 rule-based assignment.
Requirements:
Spectra library and fragmentation rules (fragmentation rules are provided)
Decision rules:
Yes
Spectra matching:
Yes
LC-MS:
Yes
Shotgun MS/MS:
Yes
5) Lipid quantification from untargeted lipidomics datasets (HRAM MS, DDA, DIA)
6) Analysis and visualization of lipidomics data