MZmine 2 features
Raw data file formats
MZmine 2 can read and process both unit mass resolution and exact mass resolution (e.g. FTMS) data in both continuous and centroided modes, including fragmentation (MSn) scans. Supported data formats are:
- mzML (version 1.0 and 1.1)
- mzXML (versions 2.0, 2.1 and 3.0)
- mzData (versions 1.04 and 1.05)
- NetCDF (no MSn support)
- Thermo RAW (only on Windows with Thermo Xcalibur version 2)
Visualization
MZmine 2 can visualize your raw data together with peak picking and identification results, which is very useful for evaluating different peak detection methods.
Chromatogram plot of 2 files
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Spectrum plot showing detected peaks
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2D plot showing detected peaks
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3D view of the data set
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3D detail of a single peak
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Peak picking
Peak detection in MZmine 2 is performed in a three-step manner: first, mass values are detected within each spectrum (several methods are available, depending on the nature of the data). In the second step, a chromatogram is constructed for each of the mass values which span over certain time range. Finally, deconvolution algorithms are applied to each chromatogram to recognize the actual chromatographic peaks.
Preview of mass detection
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Preview of peak recognition
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Observing the results
MZmine 2 can report the quantification results in table form or using charts. Direct export of the data to CSV (comma-separated) file is also possible.
| Peak list view showing the result of database identification and peak shape previews 
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Metabolite peak areas in different samples
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Peak list processing
There are several modules for further processing of peak detection results, including deisotoping, filtering and alignment. Identification of peaks can be performed by searching a custom database or by connecting to PubChem Compound database.
Statistical analysis
MZmine 2 contains basic methods for statistical analysis of processed data. However, development of these methods is not considered high priority, as processed data can be easily exported to a third-party statistical software.
Principal component analysis
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Logratio plot of two samples
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