Project management

One of the new core features of MZmine 2 is project management, which allows the user to track and store intermediate results. Each data-processing step can be performed multiple times with different parameters and the results can be observed and compared. The data processing pipeline settings (e.g., algorithms and parameters used, reference peak lists) can be stored for future applications. Direct export of the peak list data to comma-separated values (CSV) or XML files is also possible.

Raw data file format support

MZmine 2 can read and process both unit mass resolution and accurate mass resolution MS data in both continuous and centroid modes, including fragmentation (MSⁿ) scans. Raw data import is modularized and the currently supported file formats are mzML (1.0 and 1.1), mzXML (2.0, 2.1 and 3.0), mzData (1.04 and 1.05), NetCDF, and RAW format used natively by Thermo Fisher Scientific instruments (requires installation of Thermo Xcalibur). Support for other file formats can be implemented as additional plug-ins.

Data visualization

MZmine 2 includes several of visualization modules (Figure ​(Figure2),2), all of which were newly implemented for this release. Following the goal of providing the user with an intuitive interface, the visualizers automatically annotate raw data with the obtained peak picking and identification results, allowing for quick orientation when large amounts of data are being processed.

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Figure 2

Screenshot of MZmine 2 showing multiple visualization modules. The specific panels included are: (A) imported samples, (B) peak lists including single peak list contents, (C) peak shapes for an identified metabolite across multiple samples, (D) MS/MS spectrum of a metabolite, (E) combined base peak plot for multiple samples, (F) scatter plot of peak areas across two samples, (G) 2D plot of a detected peak, mass-to-charge ratio vs. retention time, (H) 3D view of a detected peak, and (I) intensity plot for specific peaks across multiple samples.

Quantitative results in the form of peak lists may be observed using a table visualizer or chart-plotting modules (Figure ​(Figure2I).2I). The scatter plot visualizer (Figure ​(Figure2F)2F) has proven to be very useful for efficient comparison of multiple samples [13].

Peak detection

Feature detection is a critical step in MS data processing. The peak detection methods and their implementations should be flexible enough to deal with great differences in data obtained from different instruments, such as variable mass resolution, chromatographic resolution and peak shape, or background noise. In MZmine 2, peak detection is performed in several customizable steps (Figure ​(Figure3).3). Previews are provided to allow for optimal selection of parameter values.

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Figure 3

Peak detection modules with previews. (A) Mass detection (centroiding) module. Recognized m/z peaks are shown in red. In the insets, details of a single m/z peak are shown, indicating the full width at half maximum approach to the m/z value calculation. (B) Fourier transform mass spectrometry shoulder peaks filter. In the preview panel, the main detected peak is indicated with the red line, while shoulder peaks are indicated with the yellow lines. (C) Peak deconvolution. Each individual recognized peak within the chromatogram is indicated by a different color. (D) Experimental peak shape modeler. A Gaussian peak model (pink) is fitted to the deconvoluted chromatographic peak's data points (blue).

In the first step (Figure ​(Figure3A),3A), each MS spectrum is processed individually and converted to pairs of m/z and intensity values (in other words, each mass spectrum is centroided). Several algorithms are provided as plug-ins, each suitable for a different type of mass spectra. The "Local maxima" algorithm is a simple algorithm suitable for demonstrating the process: it detects each local maximum in the spectrum. The "Recursive threshold" algorithm is based on an earlier method implemented in MZmine [3,4] and adds two additional parameters of minimum and maximum peak m/z width. This method reduces the false positives by avoiding detection of noise peaks. The "Wavelet transform" algorithm is particularly suitable for noisy data. It processes each spectrum using continuous wavelet transform, matching the m/z peaks to the "Mexican hat" wavelet model. This algorithm is based on a previously reported method [14]. The "Exact mass" algorithm assumes high quality spectra (high mass resolution, low noise) and determines the center of each m/z peak using the "full width at half maximum" paradigm: m/z value is placed in the middle of the line, which crosses the peak at half of the maximum intensity (as shown in the insets in Figure ​Figure3A).3A). Finally, the "Centroid" algorithm is suitable for already centroided data. It detects all data points above the specified noise level as m/z peaks.

Data obtained by Fourier transform mass spectrometry instruments provide very high mass resolution, but suffer from the presence of noise signals known as "shoulder peaks" (Figure ​(Figure3B).3B). These peaks are residues of the Fourier transform function calculated by the instrument and their intensity is usually below 5% of the intensity of the main (true) m/z peak. To remove these noise peaks, we introduced an optional filtration plug-in that builds a theoretical model (such as Gaussian or Lorentzian) with given mass resolution around each peak, and removes all noise peaks below this model. Peaks are processed in the order of decreasing intensity. In the preview (Figure ​(Figure3B),3B), the main m/z signal is indicated by the red color, while the shoulder peaks subject to removal are indicated in yellow. Again, it is possible to implement other filtration algorithms as plug-ins.

The next step consists of an algorithm that connects consecutive m/z values spanning over multiple scans into chromatogram objects. The default algorithm provided by MZmine 2 connects m/z values in the order of their intensity, with the most intense peaks connected first. A chromatogram spanning a given minimal time range is constructed for each m/z value (within user-defined tolerance). Each chromatogram is then deconvoluted into individual chromatographic peaks (Figure ​(Figure3C).3C). Several algorithms are provided as plug-ins. The "Baseline cut-off" algorithm recognizes each chromatographic peak that has an intensity above a given minimum level and spans over a given minimum time range. The "Noise amplitude" algorithm adds another parameter specifying the intensity range, which is considered noisy. The algorithm then finds the intensity level where most of the noise is concentrated and sets the baseline level to this intensity, individually for each chromatogram. Following the setting of the baseline, the procedure is the same as the "Baseline cut-off" algorithm. The Savitzky-Golay algorithm uses the smoothed second derivative of the chromatogram curve to detect the borders of individual peaks. The "Local minimum search" algorithm attempts to identify local minima in the chromatogram as border points between individual peaks. Several restrictions are placed on possible peak shapes, such as minimum absolute and relative intensities, or a minimum ratio between peak maximum and edge.

We also implemented an experimental module, which fits the (potentially noisy) set of data points of each deconvoluted peak with an ideal peak model such as Gaussian or Exponentially Modified Gaussian (Figure ​(Figure3D).3D). Such an approach may reduce the chromatographic noise between samples, but the practical applicability of this method has not yet been thoroughly validated.

Peak identification

Assignment of intuitive metabolite or peptide names to detected m/z values greatly assists with the process of data interpretation. In MZmine 2, identification of peaks can be performed either by searching a custom database of m/z values and retention times, or by connecting to an online resource such as PubChem [15], KEGG [16], METLIN [17], or HMDB [18] directly from the MZmine 2 interface (Figure ​(Figure4).4). For each ion subjected to identification, its neutral molecular mass (m_neutral) is calculated from its m/z value. For that purpose, the charge of the ion (z) can be automatically determined from its isotope pattern. Ionization mode (positive or negative) and ionization adduct (e.g. H⁺, Na⁺, K⁺, etc.) are selected by the user as parameters. Neutral mass is then calculated as m_neutral = (m/z × z) ± m_adduct, where the sign (±) is defined by the ionization mode and m_adduct is the mass of the selected ionization adduct. The neutral mass m_neutral is the primary term for database search, within user-specified tolerance. Isotopic pattern similarity can be used as a second filter to select optimal candidates, by comparing the ratios of the detected isotopes and matching isotopes from the predicted isotopic pattern of the database compound. Because the online identification module is itself modularized, support for other molecular databases can be easily added. For proteomic applications, a module allowing identification of peptide peaks using the MASCOT [19] search engine and MS/MS spectra is under development.

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Figure 4

Peak identification using the PubChem Compound database. (A) A peak list showing the row selected for identification. (B) Dialog for setting search parameters. (C) Table of candidates obtained from the database within a given mass tolerance. (D) 2D and 3D structural views of the candidate compound.

RANdom SAmple Consensus (RANSAC) aligner

The purpose of peak list alignment is to match relevant peaks across multiple samples. The original MZmine software introduced a simple alignment algorithm that first creates an empty master peak list and then aligns each peak from given peak lists (samples) to the best candidate of the master list using a two-dimensional alignment window (AW) represented by user-specified m/z and retention time tolerances. If no suitable candidate is found, a new row is created in the master list. In MZmine 2, this algorithm is referred to as the "Join aligner". One disadvantage of the Join aligner is the inability to cope with a non-linear deviation of the retention times among samples. For this purpose, we introduced a new peak list alignment method based on the RANSAC algorithm.

The RANSAC algorithm [20] is a non-deterministic iterative algorithm that estimates parameters of a mathematical model from a set of observed data, which may include outliers. The probability of obtaining a good result increases with the number of iterations. In each iteration, a random subset of observed data points is selected and a model is fit to this data. In our specific case, we used 4 points to find a non-linear model. The remaining data is tested against the fitted model and if a value fits well, it is considered a part of the model. Finally, the model is evaluated and when the iteration is finished, the model with the most data points fitted to it is considered the best.

The RANSAC method of alignment makes use of two user-defined two-dimensional windows, the RANSAC window (RW) and Alignment window (AW), respectively. The RW is defined by the m/z threshold rm₀ and retention time threshold rr₀, and AW constitutes the same m/z threshold rm₀ but a different retention time threshold ar₀. The retention time threshold in RW should be as big as the maximum observed deviation in the retention time among all peaks. The procedure for aligning a sample S_j with the master list L is as follows:

Step 1: For every row i in L, let

r_i = the average retention time of all individual peaks in the row

m_i = average m/z of all individual peaks in the row

RW_i = [(m, r) | m_i - rm₀ ≤ m ≤ m_i + rm₀ and r_i - rr₀ ≤ r ≤ r_i + rr₀], the RANSAC window for row i.

Then, for row i in L, mark all peaks in sample S_j in RW_i as candidate alignments.

Step 2: Build a scatter plot representation of all candidate alignments, and apply the RANSAC algorithm to build a candidate model for alignment. This model represents a list of matching retention times.

Step 3: Apply the locally-weighted scatterplot smoothing (LOESS) method for regression [21] on all points in the model obtained with RANSAC.

Step 4: Using this regression model, for each row i in L, predict the correction for the retention time shift to locate the new center (m_i, r'_i) of the alignment window AW_i. RANSAC alignment can correct the retention time deviation by centering the position of the AW to the correct position in the new sample.

Thus, the alignment window AW_i = [(m, r) | m_i - rm₀ ≤ m ≤ m_i + rm₀ and r'_i - ar₀ ≤ r ≤ r'_i + ar₀]

Step 5: For each row i in L, apply the Join algorithm for alignment using the alignment window AW_i.

Figure ​Figure55 shows a preview of the RANSAC alignment in MZmine 2. Each dot represents a candidate alignment of two peaks. Red dots represent those candidate alignments that were fitted to the best model (blue line).

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Figure 5

RANSAC aligner. Dialog shows preview of RANSAC alignment of two peak lists using the given parameters. Each possible candidate alignment (peak pair) within a defined m/z and retention time tolerance is shown as a dot. A model is fitted to the data (blue line) and red dots indicate those fitting to the model and therefore selected for the final alignment.