https://wiki-ms.microbe-ms.com/api.php?action=feedcontributions&feedformat=atom&user=LaschpMicrobeMS Wiki - User contributions [en]2026-10-24T11:53:08ZUser contributionsMediaWiki 1.39.2https://wiki-ms.microbe-ms.com/index.php?title=Microbial_Identification_based_on_Mass_Spectral_Libraries_and_Interspectral_Distances&diff=1244Microbial Identification based on Mass Spectral Libraries and Interspectral Distances2026-08-26T16:39:15Z<p>Laschp: /* Introduction */</p>
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<div>== Introduction ==<br />
<br />
* In the MicrobeMS software package microbial identification based on mass spectral libraries and interspectral distances, or similarity measures such as D-values, is carried out by means of the subfunction cmpr ('''c'''o'''mp'''a'''r'''e). The cmpr function is available from the ''Analysis'' menu bar (select ''compare with database''), or by pressing the button ''compare with DB'' in the ''ANALYSIS'' tab (see [[Screenshot_of_MicrobeMS|Screenshot of MicrobeMS]]). The function calculates interspectral distances (on the basis of peak tables) between spectra in a database and the experimental mass spectra. Spectra in a database, also called reference spectra, are recorded from microorganisms with a known taxonomic status and can be of two types: (i) single experimental spectra and (ii) so-called database spectra. The latter are created from a selection of experimental spectra (see section ''interspectral distances below''). <br />
<br />
[[File:Cmpr-main-figure.jpg|thumb|500px|Fig.1 Screenshot of the dialog box "''Identification analysis based on interspectral distances''"]]<br />
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[[File:in-silico-II.png|thumb|500px|Fig.2 Screenshot of the window "''Show db content'' (in silico database)"]]<br />
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[[File:Cmpr-peaks-figure.png|thumb|500px|Fig.3 Screenshot of the dialog box "''More results''"]]<br />
<br />
* Reference spectra are ranked by the ''cmpr'' function according to their spectral distances to the test spectrum such that the reference spectrum with the smallest distance (or highest score) appears on the top position of the score ranking list. As the genus, species and strain identity of the reference microorganisms used to record the database spectra are known, this allows microbial identification at certain taxonomic level. <br />
<br />
* In MicrobeMS spectral distances can be obtained by using different metrices: Euclidean distances, D-values derived from Pearson’s product-momentum correlation coefficients, covariance and Pareto scaling (see section ''interspectral distances'' below). All distance algorithms search for matching peaks in the peak tables of the test spectra and the spectra comprising the database, respectively. <br />
<br />
* The function cmpr always requires the presence of peak tables. These peak tables must be obtained from both types of spectra, test and reference spectra. Peak tables contain the m/z positions of the peaks, intensities of the unprocessed spectra, normalized intensities (also called weightings) and – in case of database spectra – frequency values of the mass peaks. Frequency values indicate how often a peak was found in the experimental spectra used to create the actual database spectrum. <br />
<br />
* An important problem of calculating interspectral distances from different MALDI-ToF mass spectra is to define the conditions at which a peak in the test spectrum matches a peak in a reference spectrum. It is known that MALDI-ToF MS generally suffers from a relatively low precision of the experimentally determined m/z peak positions. Therefore, when analyzing spectra from biological and technical replicates mass peaks of theoretically identical m/z positions may be detected at slightly varying m/z positions. To deal with such inaccuracies of the m/z positions, a program wide variable ''ppm'' has been introduced. The ppm parameter defines the maximum allowed variation between theoretically identical peaks obtained by different measurements. The ppm variable defines the width of mass regions, or m/z sections (intervals), in which spectra are subdivided: [M-ppm/2 M+ppm/2], with M being the m/z position of a given section center. Only in cases where peaks from distinct spectral measurements fall within the borders of such m/z sections they are treated as identical peaks. <br />
<br />
* Database spectra are ideally created from a defined number of experimental mass spectra, usually between 3 and 20. The procedure of creating database spectra always starts from raw experimental mass spectra and includes spectral pre-processing, peak detection followed by a statistical analysis of the peak tables. A database spectrum is essentially represented by a peak table in which the following values are stored:<br />
<ul><ul><br />
a) the average peak position of mass peaks, <br><br />
b) the mean intensities of the mass peaks (obtained from experimental mass spectra),<br><br />
c) normalized peak intensities (weightings): the sum of the normalized intensities equals 100 in a database spectrum and<br><br />
d) the frequency the peaks are found in the experimental mass spectra used to create the database spectrum.<br><br />
&nbsp; &nbsp; (see also [[Data_Format_of_Peak_List_Files#peak table format | format of peak tables]])<br />
</ul></ul><br />
<br />
* Parameters for automated spectral pre-processing and peak detection are stored in the file [[Description of MicrobeMS' main parameter file 'microbems.opt'| ''microbems.opt'']]. This file is a simple text file which can be edited by text editors like Notepad. It is required to restart MicrobeMS to initialize changes made to this file. Note that existing blocks of pre-processed spectra, or peak tables are not overwritten when creating database spectra from experimental mass spectra.<br />
<br />
== Interspectral distances ==<br />
<br />
In MicrobeMS, interspectral distances are calculated from peak tables (see above) and can be of the following types:<br />
<br />
<ol><br />
<br />
<li> Euclidean distances: probably the most commonly chosen type of distance. The Euclidean distance can be considered the geometric distance in a multidimensional space, see [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] for details.</li> &nbsp; <br><br />
<br />
<li> Pearson: Pearson distances <math forcemathmode="png">D_1(x,y)</math> between two peak tables x and y are calculated on the basis of [https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient Pearson's product momentum correlation coefficient], which is basically the [https://en.wikipedia.org/wiki/Covariance covariance <math forcemathmode="png">cov(x,y)</math>] divided by the product of their [https://en.wikipedia.org/wiki/Standard_deviation standard deviations <math forcemathmode="png">\sigma_x \sigma_y</math>], this product is also known as the total joint variance. Values of <math forcemathmode="png">r_1(x,y)</math> may vary between -1 (perfect negative linear correlation), 0 (no correlation) and 1 (perfect positive linear correlation). Equation (2) is then applied to obtain Pearson's distance <math forcemathmode="png">D_1(x,y)</math>. This distance varies between 0 (identity - perfect positive linear correlation) and 2000 (anti-correlation - perfect negative linear correlation).<br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">r_1(x,y) = {cov(x,y) \over \sigma_x \sigma_y }</math> (1), and <math forcemathmode="png">D_1(x,y) = 1000*[1-r_1(x,y)]</math> (2) </li> &nbsp; <br><br />
<br />
<li> Pareto (0.75): The Pareto-0.75 distance <math forcemathmode="png">D_{0.75}(x,y)</math> between two peak tables x and y is obtained on the basis of the Pareto-scaled correlation coefficient <math forcemathmode="png">r_{0.75}(x,y)</math> which is calculated by dividing the covariance ''cov(x,y)'' of the two vectors by the product of their standard deviations to the power of 0.75, see eqn. (3). Resulting values are then scaled by dividing them by <math forcemathmode="png">r_{0.75}(x,x)</math>. For this purpose, the test peak table vector is compared with itself. <math forcemathmode="png">D_{0.75}(x,y)</math> can be subsequently computed from equation (4). Note that the Pareto-0.75 distance can be smaller than 0 and larger than 2000. <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">r_{0.75}(x,y) = {cov(x,y) \over (\sigma_x \sigma_y)^{0.75}}</math> (3), and <math forcemathmode="png">D_{0.75}(x,y) = 1000 * [1-{r_{0.75}(x,y) \over r_{0.75}(x,x)}] </math> (4) </li> &nbsp; <br><br />
<br />
<li> Pareto (0.50): The Pareto-0.50 distance <math forcemathmode="png">D_{0.50}(x,y)</math> between two peak tables x and y is determined in a similar way to the Pareto-0.75 distance: The only difference is that the exponent value of 0.75 is replaced by a value of 0.50, cf. eqn. (5) below. Specifically, the Pareto-0.50 distance <math forcemathmode="png">D_{0.50}(x,y)</math> is obtained on the basis of the Pareto-scaled correlation coefficient <math forcemathmode="png">r_{0.50}(x,y)</math> which is calculated by dividing the covariance ''cov(x,y)'' of the two vectors by the product of their standard deviations to the power of 0.50. <math forcemathmode="png">r_{0.50}(x,y)</math> values are then scaled by dividing them by <math forcemathmode="png">r_{0.50}(x,x)</math>. For this purpose, the test peak table vector is compared with itself. <math forcemathmode="png">D_{0.50}(x,y)</math> can be subsequently computed from the following equation (6). Again, the Pareto distance can be smaller than 0 and larger than 2000. <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">r_{0.50}(x,y) = {cov(x,y) \over (\sigma_x \sigma_y)^{0.50}}</math> (5), and <math forcemathmode="png">D_{0.50}(x,y) = 1000 * [1-{r_{0.50}(x,y) \over r_{0.50}(x,x)}] </math> (6) </li> &nbsp; <br><br />
<br />
<li> Pareto (0.25): The Pareto-0.25 distance <math forcemathmode="png">D_{0.25}(x,y)</math> between two peak tables x and y is determined similarly to the Pareto-0.75 distance: The only difference is that the exponent value of 0.75 is replaced by a value of 0.25 (see above). Like the Pareto-0.75 and the Pareto-0.50 distances, values for <math forcemathmode="png">D_{0.25}(x,y)</math> can be smaller than 0 and larger than 2000.<br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">r_{0.25}(x,y) = {cov(x,y) \over (\sigma_x \sigma_y)^{0.25}}</math> (7), and <math forcemathmode="png">D_{0.25}(x,y) = 1000 * [1-{r_{0.25}(x,y) \over r_{0.25}(x,x)}] </math> (8) </li> &nbsp; <br><br />
<br />
<li> Covariance: First, the covariance ''cov(x,y)'' between the two peak table vectors x and y is calculated. Then, the covariance ''cov(x,x)'' between the test peak table vector with itself is obtained. The covariance-based distance <math forcemathmode="png">D_0(x,y)</math> is determined by equation (9). Covariance-based distances can be smaller than 0 and larger than 2000. <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">D_0(x,y) = 1000 * [1-{cov(x,y) \over cov(x,x)}] </math> (9) </li> &nbsp; <br><br />
<br />
</ol><br />
<br />
== Scores and score ranking lists ==<br />
<br />
<br />
[[File:compr-txt.png|thumb|500px|Fig.4 Dialog box "''MicrobeMS identification report''" in the text format]]<br />
<br />
[[File:in-silico-I.png|thumb|500px|Fig. 5 Screenshot of the window ''"MicrobeMS identification report''" (HTML formatted)]]<br />
<br />
For MALDI-ToF MS-based identification of unknown test organisms, the reference spectra are ranked according to their distance to the test spectrum, so that the reference spectrum with the closest distance, or highest score, is ranked first in the ''so called'' score ranking list. Since the taxonomic identities of the reference microorganisms are known, the ranking and the individual score values allow microbial identification.<br />
<br />
Score values <math forcemathmode="png">S_i</math> and log score values <math forcemathmode="png">Sld_i</math> are directly computed from the inter-spectral distance values (<math forcemathmode="png">D_1, D_{0.75}, D_{0.50}, D_{0.25}</math> and <math forcemathmode="png">D_{0}</math>) by means of the following equations:<br> &nbsp; <br><br />
<br />
:<math forcemathmode="png">S_i = 1000 - D_i</math>, if <math forcemathmode="png">S_i < 1</math> &rarr; <math forcemathmode="png">S_i = 1</math> (10), and <math forcemathmode="png">Sld_i = log_{10}(S_i) = log_{10}(1000 - D_i) = 3 - log_{10}(D_i)</math>, if <math forcemathmode="png">Sld_i < 0</math> &rarr; <math forcemathmode="png">Sld_i = 0</math> (11) <br> &nbsp; <br><br />
<br />
In consequence values of <math forcemathmode="png">S_i</math> vary between 1 (negative, no, or almost no correlation) and 1000 (perfect correlation). <math forcemathmode="png">S_i</math> can be larger than 1000 in cases of Pareto or covariance scaling and if ''w-fact'' has been set to values larger than 0 (requires an activated checkbox ''use weightings''). <br><br />
Computation of logarithmic score values <math forcemathmode="png">log_{10}(D_i)</math> produces values between 0 (negative, no, or almost no correlation), 3 (identity), or above 3 (possible only in case of Pareto and covariance scaling). In MicrobeMS score and log score values are helpful to assess and compare levels of similarity between the experimental test spectra and microbial reference spectra, i.e. database spectra.<br><br />
<br />
Score values obtained by MicrobeMS should not be compared to the score values of Bruker's MALDI Biotyper. Due to the different algorithms used - MicrobeMS score values are based on interspectral distances - MicrobeMS scores tend to be larger than the corresponding MALDI Biotyper score values. Of note, such higher scores do not indicate better matches between the experimental test and the database spectra.<br />
<br />
It is also important to be aware of the fact that the score values are dependent on the number of peaks in both the test and the database spectra. If the number of peaks is different, the equations used will tend to calculate lower score values. If the underlying MALDI-ToF mass peak tables are of different lengths, this will inevitably lead to reduced correlation values and therefore lower scores. Therefore, it is important that only peak tables are used for the calculation of the score values, in which the number of peaks in all test and database spectra is identical. Otherwise, the identification of microorganisms on the basis of score-ranking lists can lead to erroneous results.<br />
<br />
''Peak number corr factor'' (still experimental!): A factor for a still highly experimental procedure to compensate for different numbers of peaks derived from experimental test and database spectra. Obviously, the ratio between the peak numbers of the test and database spectra has a strong influence on the distance values and thus on the calculated scores. It is recommended to select a factor of 1 (default) to disable this algorithm. <br><br />
<br />
In the context of HTML reports, score values are used to color-code the analysis results. For this, a traffic light scheme is used. Analysis results greater than 562 (&rarr; log score > 2.75) are displayed in green, while values below 316 (&rarr; log score < 2.25) display in red (default settings). All other results are given by the yellow color (cf. lower table of Fig. 6). Color coding thresholds can be adjusted by editing the settings ''data.ident.scores'' of the file [[Description of MicrobeMS' main parameter file 'microbems.opt'|''microbems.opt'']].<br />
<br />
== Rank list analyses and rank list score ==<br />
<br />
[[File:Rank_list_score.jpg|thumb|500px|Fig. 6 Example of a ''score ranking list'' (bottom) and the the resulting ''rank list scores'' for microbial species ''Bacillus thuringiensis, Bacillus cereus sensu lato and Bacillus cereus'' (top)]]<br />
<br />
Score ranking lists are not always unambiguously interpretable. Interpretation of score lists can be challenging in cases where a) scores of strains of closely related species are similar, b) strains of different species are listed in non-systematic way, or c) in a mix of a) and b). For example the figure to the right (Fig. 6) shows a score ranking list of a MALDI-ToF MS analysis of a strain from ''Bacillus thuringiensis''. This species, belongs to the ''Bacillus cereus'' group ''(B. cereus'' sensu lato, s.l.) like ''Bacillus cereus'' (''B. cereus'' sensu stricto, s.s.). It is well established that ''B. cereus'' s.s. and ''B. thuringiensis'' cannot be reliably differentiated by means of MALDI-ToF MS. The provided score ranking list does not allow for an unambiguous identification, because the differences between the score values are relatively small and different reference strains of the involved bacterial species are not arranged in a systematic manner. This is the point where the so-called '''''rank list analysis''''' becomes important. Its purpose is to facilitate the interpretation of ambiguous score ranking lists. For this purpose, the species assignment of each of the listed reference spectra, as well as their ranks and score values, are automatically analyzed and summarized as a '''''rank list score''''' <math forcemathmode="png">F_{j}</math> for each of the species involved. The result of such a rank list analysis, the rank list score, is calculated using equations (12)-(15) which are given below: <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">F_{j} = \sum_{i=1}^{m_j}(Sld_{i,j}-2)*{\left({n-r_{i,j}+1 \over n}\right)}^k</math> (13), <br><br />
<br />
:In (13) <math forcemathmode="png">F_{j}</math> denotes the rank list score of the ''j-th'' species; <math forcemathmode="png">r_{i,j}</math> and <math forcemathmode="png">Sld_{i,j}</math> are the rank and the log score of the ''i-th'' strain of the ''j-th'' species, respectively. Furthermore, ''n'' equals the total number of strains in the rank list, usually 20, while ''k'' denotes an exponent (''k'' = 9.0). Log scores below 2 are set to 2 and <math forcemathmode="png">m_{j}</math> is the number of strains of species ''j'' present in the database.<br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">F_{j,max} = \sum_{i=1}^{m_j} {\left({n-r_{i,j}+1 \over n}\right)}^k</math> (12),<br><br />
<br />
:<math forcemathmode="png">F_{j,max}</math> is the maximum theoretical value, <math forcemathmode="png">F_{j}</math> can reach. This is achieved in case all strains of the species ''j'' are listed on top positions and all log scores equal 3. <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">S_{rl,j} = 1000 * {F_{j} \over F_{j,max}}</math> (14), and <br> &nbsp; <br><br />
<br />
:: <math forcemathmode="png">Sld_{rl,j} = log_{10}(S_{rl,j}) = log_{10}\left(1000 * {F_{j} \over F_{j,max}}\right)</math> (15) <br> &nbsp; <br><br />
<br />
:<math forcemathmode="png">S_{rl,j}</math> is the rank list score of species ''j'', with 0 ≤ <math forcemathmode="png">S_{rl,j}</math> ≤ 1000. <math forcemathmode="png">Sld_{rl,j}</math> denotes the respective log score. Logarithmic rank list scores can vary between values of 0 and 3.<br> &nbsp; <br><br />
<br />
Rank analyses work best when there are at least 10 strains per species in the database. If these conditions are not met, the results of ranking analyses should be interpreted with greater caution. This is particularly important in cases where only a few or even a single strain of a species is present. Rank analysis results are color coded in the HTML reports according to a traffic light scheme. Rank analysis results greater than 600 are displayed in green, while those less than 400 are displayed in red (default settings). All other results are displayed in yellow (cf. upper table of Fig. 6). The color coding thresholds can be manipulated by editing the settings ''data.ident.soi'' of the file [[Description of MicrobeMS' main parameter file 'microbems.opt'|''microbems.opt'']].<br />
<br />
== Vary calibration parameters ==<br />
<br />
The program option ''vary calibration parameters'' allows the three calibration constants ''c0, c1, and c2'' of test time-of-flight mass spectra to be varied in combination. This option generates a larger number ''n'' of mass spectra from a single test spectrum, which are then compared against the database spectra. From the ''n'' scores obtained, either a top score or the average of a few top scores is selected for the score ranking list. The procedure aims to largely eliminate errors that may arise due to insufficient calibration of the test spectra. The method is quite computationally intensive, but longer program runtimes could be significantly reduced by a suitable programming technique (vectorization of the underlying Matlab code).<br />
<br />
''Vary calibration parameters'' (checkbox): Calibration parameters are varied when this checkbox is activated. When calculating distance values between test and reference spectra, the comparison is done for a set of [''2n+1 &times; 2n+1 &times; 2n+1''] variations of three calibration constants, with ''n'' being the value chosen from the pull down menu ''var factor'' (see screenshot at the top of this page). For example, if the default value of ''n''=4 has been selected from the pull down menu, MicrobeMS will calculate in each comparison interspectral distances between the respective reference (database) spectrum and 729 variations of test spectra [9 &times; 9 &times; 9 = (2&times;4+1) &times; (2&times;4+1) &times; (2&times;4+1)] that represent the different combinations of three different calibration constants ''c0, c1, and c2''. In the easiest case (when the checkbox ''average distances'' is unchecked), identification reports will display only one - the best (highest) - match obtained from the "best" calibrated test spectrum.<br><br />
''Vary calibration range factor'': This factor defines the range in which the calibration constants are allowed to vary. High values indicate a wide range and vice versa. Select high values for poorly calibrated spectra. Note that a high calibration range factor can lead to accidental high values from unrelated microbial taxa.<br><br />
''Max number of variations'': This parameter is useful to reduce the computational load when calibration constants are varied. If the number of variations is larger than indicated, a distance-based algorithm removes similar combinations of calibration constants.<br><br />
''Average distances'': If checked, an average of the best score values for the given test spectrum (rather than just the highest score) will be determined. This is intended to increase the robustness of the calculated scores. The number of scores used for averaging is set to 1% of the possible combinations of calibration constants, at minimum 4.<br><br />
<br />
== Use weightings ==<br />
<br />
In MicrobeMS, normalized intensity values of peaks are referred to as ''weightings''. Peak weightings are obtained after peak detection, which is mostly based on pre-processed mass spectra with pre-processing usually involving smoothing, baseline correction and a special form of normalization (adapted 1-norm), see also the section on [[Spectral Pre-processing|spectral pre-processing]]. With peak detection, the intensities of all extracted peaks are normalized a second time, whereby the sum of the intensities of all detected peaks being set to 100. The intensity values generated in this way are referred to as ''weightings''.<br><br />
In MicrobeMS, interspectral distances can be calculated from barcode spectra, where all peaks weightings are equal. In these cases the weightings of all peaks are set to an average weighting value. For spectra containing 30 peaks this would be a value of 3.333 as the the intensities' sum should always give 100. Other options for distance calculation includes the use of of unmodified weightings. In the third case, peak weightings are scaled between barcode intensities and unmodified weightings.<br><br />
If the ''use weightings'' check box is checked, interspectral distances will be calculated on the basis of weighting values. Otherwise, distances are calculated from barcode spectra. In cases where the ''use weightings'' check box is checked, the ''w-fact'' edit box, the scaling factor wf, becomes active. The scaling factor can vary between 0 (barcode spectra) and 1 (unmodified or full weightings) and defines the relative influence of the peak weighting values when creating spectrum vectors for calculating distance values. For this, the following equation is used: <br> &nbsp; <br><br />
<br />
:<math forcemathmode="png">I_{weight,i} = (1-wf)*\bar{I}_{exp} + wf*I_{exp,i} </math> (16), where <br> &nbsp; <br><br />
<br />
::<math forcemathmode="png">I_{weight,i}</math> is the scaled weighting of the ''i''-th mass peak<br><br />
::<math forcemathmode="png">wf</math> denotes the scaling factor with 0 &le; wf &le; 1<br> <br />
::<math forcemathmode="png">\bar{I}_{exp}</math> denotes the mean peak weighting of the given spectrum<br><br />
::<math forcemathmode="png">I_{exp,i}</math> the weighting of the i-th peak<br> &nbsp; <br><br />
<br />
In cases where <math forcemathmode="png">wf</math> equals 0, all mass peaks of the given spectrum are assigned to the mean weighting value. Weightings intensities are retained if <math forcemathmode="png">wf</math> equals 1. In all other cases (0 < <math forcemathmode="png">wf</math> < 1), peak weightings are scaled between these two states.<br />
<br />
== Visualization and interpretation of the results ==<br />
<br />
The results of the pattern matching analyses are provided as score ranking lists and rank list reports, either in a text, or a HTML format. In score ranking lists, the top matching database entries are displayed on top positions. Further records are listed below according to the scores achieved (see Fig. 5). Rank lists analyses are available only in the HTML-formatted reports. <br />
<br />
While reports in the simple text format cannot be printed, HTML reports are printable for documentation purposes by using the appropriate function of the web browser software (Microsoft Internet Explorer, Mozilla Firefox, Opera, etc.). In cases where a pdf printer driver is available reports can be directly converted into a pdf format. Furthermore, all HTML reports are stored per default in a subfolder ''/report'' which is automatically created in the program's root directory (Windows). The name of the HTML report file will be of the format ''report-cmpr-DAY-MONTH-YEAR-HOUR-MIN-SEC.html'', for example ''report-cmpr-19-Jun-2024-11-36-39.html''.<br />
<br />
== Automated workflow for the comparison of mass spectra against a spectral database ==<br />
<br />
MicrobeMS allows identification of microorganisms based on MALDI-ToF mass spectra and mass spectral libraries by an automated and a manual workflow. This section describes the necessary procedures and steps required for automated identification.<br />
<ol><br />
<br />
<li> Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' menu bar.</li> <br />
<br />
<li> For identification select the respective spectra in the listbox in the top left corner (the listbox is labeled by ''MicrobeMS spectra ID`s''). To select multiple spectra hold the <shift> key while selecting.</li> <br />
<br />
<li> Start the automated identification procedure by pressing the button ''standard ID'' in the ''ANALYSIS'' tab (bottom of the main figure), or by choosing ''standard ID'' from the ''Analysis'' menu bar. The shortcut for this function is <Shift> + I. MicrobeMS performs then quality tests, automated pre-processing and auto peak picking using the parameters defined in the configuration file of MicrobeMS, [[Description of MicrobeMS' main parameter file 'microbems.opt'|''microbems.opt'']]. Note that existing pre-processing data and peak tables are not overwritten by this function.</li> <br />
<br />
<li> When pre-processing / peak detection has been completed MicrobeMS will load the mass spectral database defined in ''microbems.opt'' and open a figure labeled as ''identification analysis based on interspectral distances'' (see the section ''Introduction'' at the top of this page. If the database cannot be loaded (e.g. because of wrong settings in ''microbems.opt'') the programs offers to load this file manually.</li> <br />
<br />
<li> In the identification window modify the parameters and settings used for distance calculation then press ''compare'' (bottom, left). Press this button immediately to start the identification procedure with the default settings. Depending on the number of spectra and the size of the database the computation time may vary between a few seconds and several minutes. A progress indicator will be shown to give an idea of the work remaining. For a description of the parameters and settings see the section above.</li> <br />
<br />
<li> When classification has been finished the buttons ''more results'', ''Excel reports'', ''text reports'' and ''HTML reports'' will be activated. Press either of them to see the reports (please refer to the section ''Visualization and Interpretation of the Results'' for more details).</li> <br />
<br />
</ol><br />
<br />
== Manual workflow for the comparison of test spectra against a mass spectral database ==<br />
<br />
In this chapter the manual workflow for identifying microorganisms based on their MALDI-ToF mass spectra and mass spectral libraries is described.<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' menu bar.<br />
<br />
2. Manual spectral pre-processing: select first the respective spectra in the listbox in the top left corner (the listbox is labeled by ''MicrobeMS spectra ID`s''). Hold the <shift> key to select multiple spectra while selecting. Spectral pre-processing can be started by pressing the appropriate buttons of the functions ''smooth'' (smoothing of spectra), ''baseline'' (baseline subtration), ''normalize'' (normalization), or calibrate (auto-calibration). Additional pre-processing procedures which can be applied to the spectra before peak picking are ''cut spectra'' and ''reduce resolution''. Both functions are available from the ''Pre-processing'' menu bar. Recommended spectral pre-processing routines before peak detection are (Bruker spectra in the m/z range 2000 - 20,000):<br />
a) Smoothing with 21 smoothing points <br />
b) Baseline subtraction (AsLS, default settings)<br />
c) Normalization (no parameters required)<br />
In selected cases additional pre-processing procedures may be useful.<br />
<br />
3. Perform manual [[Peak Detection|peak detection]]<br />
<br />
4. Note that spectra selected for identification should contain valid peak tables. Spectra without associated peak table cannot be processed<br />
<br />
5. Start the identification procedure by pressing the button ''compare with DB'' in the ''ANALYSIS'' tab (bottom of the main figure), or by choosing ''compare with database'' from the ''Analysis'' menu bar. The shortcut for this function is <Shift> + H. MicrobeMS will then open a figure labeled as ''Identification analysis based on interspectral distances'' (see the section ''Introduction'' and Fig.1 at the top of this page). <br />
<br />
6. Load a mass spectral database by pressing the ''load'' button. After loading, the content of the database can be displayed by pressing the button ''show DB content''. Use ''unload'' to unload the database.<br />
<br />
7. In the identification window modify the parameters and settings used for distance calculation. Then press ''compare'' (bottom, right). Press this button immediately to start the identification procedure with the default settings. Depending on the number of spectra and the size of the database the computation time may vary between a few seconds and several minutes. A progress indicator will be shown to give an idea of the work remaining. For a description of the parameters and settings see the section above.<br />
<br />
8. When classification has been finished the buttons ''more results'', ''Excel reports'', ''text reports'' and ''HTML reports'' will be activated. Press either of them to see the reports (please refer to the section ''Visualization and Interpretation of the Results'' for more details).<br />
<br />
== Useful links ==<br />
<br />
* Example of a HTML formatted identification report: https://wiki.microbe-ms.com/uploads/report-maldireport-15-Dec-2024-11-06-14.html<br />
* [[Description of MicrobeMS' main parameter file 'microbems.opt'|Description of how to modify the default settings for parameters used for auto-preprocessing and auto-peak detection in the microbial identification workflow]]<br />
* [[Create database spectra|Create database spectra from series of microbial mass spectra]]<br />
* [[Data_Format_of_Peak_List_Files#structure array dbs | Description of the format of database spectra]]<br />
* [[Data_Format_of_Peak_List_Files#peak table format | Description of the format of peak tables]]<br />
* [[Format_of_Identification_Report_Files_(MS Excel,Matlab)|Description of identification report files (*.xls, *.mat)]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Screenshot_of_MicrobeMS&diff=1243Screenshot of MicrobeMS2026-08-26T16:36:52Z<p>Laschp: </p>
<hr />
<div>When MicrobeMS is started, the user is presented with two separate windows: the main user dialog box (see below) and a command line window. The former allows interaction with the program, while the latter displays input parameters and non-standard errors. These messages are stored in a log file, [[The Log-File (logfile.txt)|logfile.txt]].<br />
<br />
[[File:MicrobeMS-Main-Figure.png|1000px|left|thumb|alt=Screenshot of main window of MicrobeMS v.088 (Debian Linux)| Screenshot of main window of MicrobeMS v.088]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1242Publications with MicrobeMS2026-08-26T16:28:12Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="39" reversed="reversed"><br />
<br />
<li> Krull, N.K., M. Strobel, J. Saulog, L. Zaroubi, et al.<br><br />
IDBac: an open-access web platform to identify bacteria and analyze relationships in culture collections using MALDI-TOF mass spectrometry.<br><br />
<i>Nat Commun</i>, <b>2026</b>. 17(1).<br><br />
https://doi.org/10.1038/s41467-026-75579-4</li><br />
&nbsp;<br><br />
<br />
<li><br />
Dolias, G., O. Bragina, A. Udal, K. Zovo, et al.<br><br />
Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence.<br><br />
<i>Sci Rep</i>, <b>2026</b>. 16(1).<br><br />
https://doi.org/10.1038/s41598-026-54426-y</li><br />
&nbsp;<br><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><br />
<i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br><br />
Bacterial Typing Methods from Past to Present: A Comprehensive Overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for Pathogenic Bacteria Analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><br />
<i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic Fingerprinting of Neotropical Hard Tick Species (Acari: Ixodidae) Using a Self-Curated Mass Spectra Reference Library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to Species Level of Live Single Microalgal Cells from Plankton Samples with Matrix-free Laser/Desorption Ionization Mass Spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089</li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a Novel Species of the Burkholderia cepacia Complex Isolated from Hospital Settings and Agricultural Soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.</li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?<br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/28094091</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.</li><br />
&nbsp; <br><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br><br />
Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF MS for the Identification of Highly Pathogenic Bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid Characterisation of Klebsiella oxytoca Isolates from Contaminated Liquid Hand Soap Using Mass Spectrometry, FTIR and Raman Spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.</li><br />
&nbsp; <br><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to Discriminate Acinetobacter baumannii Clones Under Routine Experimental Conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.</li><br />
&nbsp; <br><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial Species and their Associations with Acute and Chronic Mastitis in Suckler Ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient Discriminatory Power of MALDI-TOF Mass Spectrometry for Typing of Enterococcus faecium and Staphylococcus aureus Isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia Using MALDI-TOF Mass Spectrometry and Chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid Identification of Burkholderia cepacia Complex Species Including Strains of the Novel Taxon K, Recovered from Cystic Fibrosis Patients by Intact Cell MALDI-ToF Mass Spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF Mass Spectrometry Compatible Inactivation Method for Highly Pathogenic Microbial Cells and Spores.<br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1241Publications with MicrobeMS2026-08-26T16:26:36Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="39" reversed="reversed"><br />
<br />
<li> Krull, N.K., M. Strobel, J. Saulog, L. Zaroubi, et al.<br><br />
IDBac: an open-access web platform to identify bacteria and analyze relationships in culture collections using MALDI-TOF mass spectrometry.<br><br />
<i>Nat Commun</i>, <b>2026</b>. 17(1).<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/42637730</li><br />
&nbsp;<br><br />
<br />
<li><br />
Dolias, G., O. Bragina, A. Udal, K. Zovo, et al.<br><br />
Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence.<br><br />
<i>Sci Rep</i>, <b>2026</b>. 16(1).<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/42634019</li><br />
&nbsp;<br><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><br />
<i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br><br />
Bacterial Typing Methods from Past to Present: A Comprehensive Overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for Pathogenic Bacteria Analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><br />
<i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic Fingerprinting of Neotropical Hard Tick Species (Acari: Ixodidae) Using a Self-Curated Mass Spectra Reference Library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to Species Level of Live Single Microalgal Cells from Plankton Samples with Matrix-free Laser/Desorption Ionization Mass Spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089</li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a Novel Species of the Burkholderia cepacia Complex Isolated from Hospital Settings and Agricultural Soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.</li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?<br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/28094091</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.</li><br />
&nbsp; <br><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br><br />
Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF MS for the Identification of Highly Pathogenic Bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid Characterisation of Klebsiella oxytoca Isolates from Contaminated Liquid Hand Soap Using Mass Spectrometry, FTIR and Raman Spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.</li><br />
&nbsp; <br><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to Discriminate Acinetobacter baumannii Clones Under Routine Experimental Conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.</li><br />
&nbsp; <br><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial Species and their Associations with Acute and Chronic Mastitis in Suckler Ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient Discriminatory Power of MALDI-TOF Mass Spectrometry for Typing of Enterococcus faecium and Staphylococcus aureus Isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia Using MALDI-TOF Mass Spectrometry and Chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid Identification of Burkholderia cepacia Complex Species Including Strains of the Novel Taxon K, Recovered from Cystic Fibrosis Patients by Intact Cell MALDI-ToF Mass Spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF Mass Spectrometry Compatible Inactivation Method for Highly Pathogenic Microbial Cells and Spores.<br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=How_to_Obtain_a_License&diff=1240How to Obtain a License2026-07-31T15:52:31Z<p>Laschp: </p>
<hr />
<div><br />
<br />
<br />
Once you have accepted all terms of the license agreement you can download a free version of MicrobeMS. In order to run MicrobeMS an additional license key file (''keygen.gen'') must be obtained and copied into the toolbox directory of MicrobeMS. This individual license key is NOT included in the MicrobeMS installation package and will be send to you only on request. Please e-mail your request together with your name and an institutional address to the following e-mail address: <br />
<br />
'''[mailto:lasch@microbe-ms.com lasch@microbe-ms.com]'''<br />
<br />
The license key will be valid for three years (MicrobeMS v.0.82 and later). <br />
<br />
The license key for the '''stand-alone version''' (Windows 64-bit) should be copied into the root directory of MicrobeMS, usually ''C:\Users\Public\Documents\Matlab'' (v. 0.93, note that administrator rights may be required).<br />
<br />
In case of the '''pcode toolbox edition''' of MicrobeMS the license key should be copied either into the directory ''{userpath}\Matlab'' (usually 'C:\User\YourWindowsUserName\Documents\Matlab'), or directly into the MicrobeMS toolbox directory, usually 'C:\User\YourWindowsUserName\Documents\Matlab\mass'.</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=How_to_Obtain_a_License&diff=1239How to Obtain a License2026-07-31T15:52:02Z<p>Laschp: </p>
<hr />
<div><br />
<br />
<br />
Once you have accepted all terms of the license agreement you can download a free version of MicrobeMS. In order to run MicrobeMS an additional license key file (''keygen.gen'') must be obtained and copied into the toolbox directory of MicrobeMS. This individual license key is NOT included in the MicrobeMS installation package and will be send to you only on request. Please e-mail your request together with your name and an institutional address to the following e-mail address: <br />
<br />
'''[mailto:lasch@microbe-ms.com lasch@microbe-ms.com]'''<br />
<br />
The license key will be valid for three years (MicrobeMS v.0.82 and later). <br />
<br />
In case of the '''pcode toolbox edition''' of MicrobeMS the license key should be copied either into the directory ''{userpath}\Matlab'' (usually 'C:\User\YourWindowsUserName\Documents\Matlab'), or directly into the MicrobeMS toolbox directory, usually 'C:\User\YourWindowsUserName\Documents\Matlab\mass'.<br />
<br />
The license key for the '''stand-alone version''' should be copied into the root directory of MicrobeMS, usually ''C:\Users\Public\Documents\Matlab'' (v. 0.93, note that administrator rights may be required).</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=How_to_Obtain_a_License&diff=1238How to Obtain a License2026-07-31T15:51:11Z<p>Laschp: </p>
<hr />
<div><br />
<br />
<br />
Once you have accepted all terms of the license agreement you can download a free version of MicrobeMS. In order to run MicrobeMS an additional license key file ('keygen.gen') must be obtained and copied into the toolbox directory of MicrobeMS. This individual license key is NOT included in the MicrobeMS installation package and will be send to you only on request. Please e-mail your request together with your name and an institutional address to the following e-mail address: <br />
<br />
'''[mailto:lasch@microbe-ms.com lasch@microbe-ms.com]'''<br />
<br />
The license key will be valid for three years (MicrobeMS v.0.82 and later). <br />
<br />
In case of the '''pcode toolbox edition''' of MicrobeMS the license key should be copied either into the directory ''{userpath}\Matlab'' (usually 'C:\User\YourWindowsUserName\Documents\Matlab'), or directly into the MicrobeMS toolbox directory, usually 'C:\User\YourWindowsUserName\Documents\Matlab\mass'.<br />
<br />
The license key for the '''stand-alone version''' should be copied into the root directory of MicrobeMS, usually ''C:\Users\Public\Documents\Matlab'' (v. 0.93, note that administrator rights may be required).</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=How_to_Obtain_a_License&diff=1237How to Obtain a License2026-07-31T15:50:54Z<p>Laschp: </p>
<hr />
<div><br />
<br />
<br />
Once you have accepted all terms of the license agreement you can download a free version of MicrobeMS. In order to run MicrobeMS an additional license key file ('keygen.gen') must be obtained and copied into the toolbox directory of MicrobeMS. This individual license key is NOT included in the MicrobeMS installation package and will be send to you only on request. Please e-mail your request together with your name and an institutional address to the following e-mail address: <br />
<br />
'''[mailto:lasch@microbe-ms.com lasch@microbe-ms.com]'''<br />
<br />
The license key will be valid for three years (MicrobeMS v.0.82 and later). <br />
<br />
In case of the '''pcode toolbox edition''' of MicrobeMS the license key should be copied either into the directory ''{userpath}\Matlab'' (usually 'C:\User\YourWindowsUserName\Documents\Matlab'), or directly into the MicrobeMS toolbox directory, usually 'C:\User\YourWindowsUserName\Documents\Matlab\mass'.<br />
<br />
The license key for the '''stand-alone version''' should be copied into the root directory of MicrobeMS, usually '''C:\Users\Public\Documents\Matlab''' (v. 0.93, note that administrator rights may be required).</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=How_to_Obtain_a_License&diff=1236How to Obtain a License2026-07-31T15:50:34Z<p>Laschp: </p>
<hr />
<div><br />
<br />
<br />
Once you have accepted all terms of the license agreement you can download a free version of MicrobeMS. In order to run MicrobeMS an additional license key file ('keygen.gen') must be obtained and copied into the toolbox directory of MicrobeMS. This individual license key is NOT included in the MicrobeMS installation package and will be send to you only on request. Please e-mail your request together with your name and an institutional address to the following e-mail address: <br />
<br />
'''[mailto:lasch@microbe-ms.com lasch@microbe-ms.com]'''<br />
<br />
The license key will be valid for three years (MicrobeMS v.0.82 and later). <br />
<br />
In case of the '''pcode toolbox edition''' of MicrobeMS the license key should be copied either into the directory ''{userpath}\Matlab'' (usually 'C:\User\YourWindowsUserName\Documents\Matlab'), or directly into the MicrobeMS toolbox directory, usually 'C:\User\YourWindowsUserName\Documents\Matlab\mass'.<br />
<br />
The license key for the '''stand-alone version''' should be copied into the root directory of MicrobeMS, usually 'C:\Users\Public\Documents\Matlab' (v. 0.93, note that administrator rights may be required).</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1235Publications with MicrobeMS2026-06-15T16:29:44Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><br />
<i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br><br />
Bacterial Typing Methods from Past to Present: A Comprehensive Overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for Pathogenic Bacteria Analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><br />
<i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic Fingerprinting of Neotropical Hard Tick Species (Acari: Ixodidae) Using a Self-Curated Mass Spectra Reference Library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to Species Level of Live Single Microalgal Cells from Plankton Samples with Matrix-free Laser/Desorption Ionization Mass Spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089</li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a Novel Species of the Burkholderia cepacia Complex Isolated from Hospital Settings and Agricultural Soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.</li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?<br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/28094091</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.</li><br />
&nbsp; <br><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br><br />
Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF MS for the Identification of Highly Pathogenic Bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid Characterisation of Klebsiella oxytoca Isolates from Contaminated Liquid Hand Soap Using Mass Spectrometry, FTIR and Raman Spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.</li><br />
&nbsp; <br><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to Discriminate Acinetobacter baumannii Clones Under Routine Experimental Conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.</li><br />
&nbsp; <br><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial Species and their Associations with Acute and Chronic Mastitis in Suckler Ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient Discriminatory Power of MALDI-TOF Mass Spectrometry for Typing of Enterococcus faecium and Staphylococcus aureus Isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia Using MALDI-TOF Mass Spectrometry and Chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid Identification of Burkholderia cepacia Complex Species Including Strains of the Novel Taxon K, Recovered from Cystic Fibrosis Patients by Intact Cell MALDI-ToF Mass Spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF Mass Spectrometry Compatible Inactivation Method for Highly Pathogenic Microbial Cells and Spores.<br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1234Publications with MicrobeMS2026-06-15T16:26:20Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><br />
<i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br><br />
Bacterial Typing Methods from Past to Present: A Comprehensive Overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for Pathogenic Bacteria Analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><br />
<i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic Fingerprinting of Neotropical Hard Tick Species (Acari: Ixodidae) Using a Self-Curated Mass Spectra Reference Library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to Species Level of Live Single Microalgal Cells from Plankton Samples with Matrix-free Laser/Desorption Ionization Mass Spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089</li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a Novel Species of the Burkholderia cepacia Complex Isolated from Hospital Settings and Agricultural Soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.</li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?<br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/28094091</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.</li><br />
&nbsp; <br><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br><br />
Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF MS for the Identification of Highly Pathogenic Bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid Characterisation of Klebsiella oxytoca Isolates from Contaminated Liquid Hand Soap Using Mass Spectrometry, FTIR and Raman Spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.</li><br />
&nbsp; <br><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii Clones Under Routine Experimental Conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.</li><br />
&nbsp; <br><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial Species and their Associations with Acute and Chronic Mastitis in Suckler Ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores.<br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1233Publications with MicrobeMS2026-06-15T16:17:33Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for pathogenic bacteria analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br></li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br></li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria? <br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455. <br><br />
https://www.ncbi.nlm.nih.gov/pubmed/28094091</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial species and their associations with acute and chronic mastitis in suckler ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1232Publications with MicrobeMS2026-06-15T16:16:24Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for pathogenic bacteria analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br></li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br></li><br />
&nbsp; <br><br />
<br />
<li><br />
Sauget, M., Valot, B., Bertrand, X., Hocquet, D.<br><br />
Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria? <br><br />
<i>Trends in Microbiology</i> <b>2017</b> 25(6): 447–455. <br><br />
</li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial species and their associations with acute and chronic mastitis in suckler ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1231Publications with MicrobeMS2026-06-15T16:12:28Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<br />
<li><br />
test<br />
</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>, <b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for pathogenic bacteria analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br></li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br></li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial species and their associations with acute and chronic mastitis in suckler ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1230Publications with MicrobeMS2026-06-15T16:11:45Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><br />
<br />
<ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br><br />
MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z</li><br />
&nbsp; <br><br />
<br />
<br />
<li><br />
test<br />
</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br><br />
Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .</li><br />
&nbsp; <br><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br><br />
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><br />
<i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.</li><br />
&nbsp; <br><br />
<br />
<li>Yang Y.,<br><br />
Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><br />
<i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.</li><br />
&nbsp; <br><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br><br />
NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><br />
<i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.</li><br />
&nbsp; <br><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br><br />
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations.<br><br />
<i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.</li><br />
&nbsp; <br><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview.<br><br />
<i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.</li><br />
&nbsp; <br><br />
<br />
<li>Qiao, L.,<br><br />
MALDI-TOF MS for pathogenic bacteria analysis.<br><br />
<i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br><br />
Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.<br><br />
<i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.</li><br />
&nbsp; <br><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br><br />
Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.<br><br />
<i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.</li><br />
&nbsp; <br><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br><br />
Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry.<br><br />
<i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.</li><br />
&nbsp; <br><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br><br />
Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data.<br><br />
<i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br></li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br><br />
Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils.<br><br />
<i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI) <br><br />
<i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br></li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment.<br><br />
<i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts.<br><br />
<i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br><br />
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI).<br><br />
<i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br><br />
Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains.<br><br />
<i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br><br />
Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review.<br><br />
<i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br><br />
Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy.<br><br />
<i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br><br />
Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions.<br><br />
<i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br><br />
Bacterial species and their associations with acute and chronic mastitis in suckler ewes.<br><br />
<i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br><br />
Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.<br><br />
<i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br><br />
Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.<br><br />
<i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P. and D. Naumann,<br><br />
MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms.<br><br />
<i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br><br />
Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics.<br><br />
<i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br><br />
Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry.<br><br />
<i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br><br />
Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks.<br><br />
<i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br><br />
MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><br />
<i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1229Publications with MicrobeMS2026-06-15T16:02:28Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<br />
<li><br />
test<br />
</li><br />
<br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> </li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br></li><br />
&nbsp; <br><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br></li><br />
&nbsp; <br><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br></li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.</li><br />
&nbsp; <br><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.</li><br />
&nbsp; <br><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.</li><br />
&nbsp; <br><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.</li><br />
&nbsp; <br><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1228Publications with MicrobeMS2026-06-15T15:59:50Z<p>Laschp: </p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="37" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br><br />
A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria.<br><br />
<i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<br />
<li><br />
test<br />
</li><br />
<br />
<li>Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.<br><br />
Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.<br><br />
<i>Frontiers in Microbiology</i>, <b>2020</b> 11(1432): p. 1432.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32695084</li><br />
&nbsp; <br><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> </li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
*****<br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br>&nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1227Publications with MicrobeMS2026-05-28T06:11:21Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="35" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br>A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <br><i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> &nbsp; <br></li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
*****<br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br>&nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1226Publications with MicrobeMS2026-05-28T06:09:47Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="34" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br>A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <br><i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> &nbsp; <br></li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
*****<br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv - biorxiv.org</i>, <b> Dec 10, 2019</b>.<br><br />
https://doi.org/10.1101/870089<br>&nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1225Publications with MicrobeMS2026-05-28T06:06:06Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="34" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv - biorxiv.org</i>, <b> May 12, 2026</b>.<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br>A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <br><i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> &nbsp; <br></li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv</i>, <b>2019</b>.<br><br />
https://doi.dx.org/10.1101/870089 .<br> &nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1224Publications with MicrobeMS2026-05-28T06:04:26Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="34" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv</i>, <b>2026</b>. (preprint), not peer reviewed)<br><br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br>A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <br><i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> &nbsp; <br></li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv</i>, <b>2019</b>.<br><br />
https://doi.dx.org/10.1101/870089 .<br> &nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Publications_with_MicrobeMS&diff=1223Publications with MicrobeMS2026-05-28T06:04:07Z<p>Laschp: /* Publications in which MicrobeMS has been used or mentioned */</p>
<hr />
<div>== Acknowledgements ==<br />
<br />
MicrobeMS is a software project that has been developed by [http://www.peter-lasch.de Peter Lasch] at the [http://www.rki.de/EN/Content/Institute/DepartmentsUnits/CenterBioSafety/zbs6/zbs6_node.html ''Proteomics and Spectroscopy''] unit at the [http://www.rki.de ''Robert Koch-Institute''] (Berlin/Germany). MicrobeMS is not open source but is provided for free, for testing or non-commercial use. Please send us references to any publications, presentations, or successful funding applications that make use of the MicrobeMS software, or MicrobeMS data sets ([mailto:Lasch@microbe-ms.com e-mail)]). <br />
<br />
<br />
We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:<br />
<br />
<ul><ul><br />
{| class="wikitable" width=800<br />
|-<br />
| [http://www.ncbi.nlm.nih.gov/pubmed/26063856 Identification of Highly Pathogenic Microorganisms using MALDI-TOF Mass Spectrometry - Results of an Inter-Laboratory Ring Trial]. Lasch P, Wahab T, Weil S, Pályi B, Tomaso H, Zange S, Granerud BK, Drevinek M, Kokotovic B, Wittwer M, Pflüger V, Di Caro A, Stämmler M, Grunow R, Jacob D. ''J Clin Microbiol''. '''2015'''. 53(8):2632-40. doi:10.1128/JCM.00813-15<br />
|}<br />
&nbsp; <br><br />
</ul></ul><br />
<br />
== Publications in which MicrobeMS has been used or mentioned == <br />
&nbsp;<br><ol start="34" reversed="reversed"><br />
<br />
<li>Lasch, P.<br>MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.<br><i>bioRxiv</i>, <b>2026</b>. (preprint), not peer reviewed)<br>.<br />
https://doi.org/10.64898/2026.05.08.723807 <br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics.<br><i>Mol Cell Proteomics</i>, <b>2026</b>. 101539.<br><br />
https://doi.org/10.1016/j.mcpro.2026.101539<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al.,<br>A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <br><i>Sci Data</i>, <b>2025</b>. 12(1): p. 187.<br><br />
https://doi.org/10.1038/s41597-025-04504-z<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., <br>Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. <br><i>bioRxiv - biorxiv.org</i>, <b> Oct 28, 2024</b>.<br><br />
https://doi.org/10.1101/2024.09.09.612008 .<br> &nbsp; <br></li><br />
<br />
<li>Collins, T.J., Muste, C., Owens, K.G.<br>Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. <br><i>J Am Soc Mass Spectrom</i>, <b>2024</b>. 35, 6, 1352–1362.<br><br />
https://dx.doi.org/10.1021/jasms.4c00101.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 4.1 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>,<b> Mar 6, 2023.</b> <br><br />
https://doi.org/10.5281/zenodo.7702374.<br> &nbsp; <br></li><br />
<br />
<li>Yang Y.,<br>Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development.<br><i> Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry</i>,<b>06 Oct 2023</b><br><br />
https://doi.org/10.1039/BK9781837670338-00045.<br> &nbsp; <br></li><br />
<br />
<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X.,<br>NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.<br><i> IEEE J Biomed Health Inform </i> 27(3),1644-1653). <b>07 Feb. 2023</b>.<br><br />
https://doi.org/10.1109/JBHI.2022.3229473.<br> &nbsp; <br></li><br />
<br />
<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z.,<br>Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. <i> Current Bioinformatics.</i> 18(6):497-508(12). <b>2023</b>.<br><br />
https://doi.org/10.2174/1574893618666230316113621.<br> &nbsp; <br></li><br />
<br />
<li>Ramadan, A.A.,<br>Bacterial typing methods from past to present: A comprehensive overview. <br><i>Gene Reports</i>, <b>2022</b>. 29: p. 101675.<br><br />
https://dx.doi.org/10.1016/j.genrep.2022.101675.<br> &nbsp; <br></li><br />
<br />
<li>Qiao, L.,<br>MALDI-TOF MS for pathogenic bacteria analysis. <br><i>International Journal of Mass Spectrometry</i>, <b>2022</b>. 482: p. 116935.<br><br />
https://dx.doi.org/10.1016/j.ijms.2022.116935.<br> &nbsp; <br></li><br />
<br />
<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al.,<br>Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. <br><i>Anal Chem</i>, <b>2021</b>. 93(44): p. 14599-14608.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/34697938.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. <br><i>Mol Cell Proteomics</i>, <b>2020</b>. 19(12): p. 2125-2139.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32998977.<br> &nbsp; <br></li><br />
<br />
<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al.,<br>Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. <br><i>PLoS Negl Trop Dis</i>, <b>2020</b>. 14(10): p. e0008849.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/33108372.<br> &nbsp; <br></li><br />
<br />
<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al.,<br>Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. <br><i>Metabolomics</i>, <b>2020</b>. 16(3): p. 28.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/32090296.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger,<br>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. <br><i>bioRxiv</i>, <b>2019</b>.<br><br />
https://doi.dx.org/10.1101/870089 .<br> &nbsp; <br></li><br />
<br />
<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al.,<br>Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. <br><i>Int J Syst Evol Microbiol</i>, <b>2018</b>. 68(1): p. 14-20.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29095137.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2018</b>. November 30, 2018.<br><br />
https://dx.doi.org/10.5281/zenodo.1880975.<br> &nbsp; <br></li><br />
<br />
<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al.,<br>Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. <br><i>Anal Chem</i>, <b>2017</b>. 89(22): p. 12556-12561.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/29086558.<br> &nbsp; <br></li><br />
<br />
<li>Meyer, B., A. Rabenstein, and J. Kuever,<br>Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. <br><i>Biology of Microorganisms on Grapes, in Must and in Wine</i>, <b>2017</b>: p. 659-701.<br><br />
https://dx.doi.org/10.1007/978-3-319-60021-5_27.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2017</b>. May 23, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.582602.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Stämmler, and A. Schneider,<br>A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). <br><i>Zenodo</i>, <b>2016</b>. Oct 27, 2017.<br><br />
http://dx.doi.org/10.5281/zenodo.163517.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner,<br>Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. <br><i>In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.)</i>, <b>2016</b>. Springer International Publishing: p. 203-234.<br><br />
http://dx.doi.org/10.1007/978-3-319-26070-9_8.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger,<br>Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.013.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob,<br>Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. <br><i>TrAC Trends in Analytical Chemistry</i>, <b>2016</b>. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119.<br><br />
http://dx.doi.org/10.1016/j.trac.2016.04.012.<br> &nbsp; <br></li><br />
<br />
<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al.,<br>Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. <br><i>Faraday Discuss</i>, <b>2016</b>. 187: p. 353-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/27053001.<br> &nbsp; <br></li><br />
<br />
<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al.,<br>Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. <br><i>Front Microbiol</i>, <b>2015</b>. 6: p. 481.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/26042113.<br> &nbsp; <br></li><br />
<br />
<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al.,<br>Bacterial species and their associations with acute and chronic mastitis in suckler ewes. <br><i>J Dairy Sci</i>, <b>2015</b>. 98(10): p. 7025-33.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26277313.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al.,<br>Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. <br><i>J Clin Microbiol</i>, <b>2015</b>. 53(8): p. 2632-40.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/26063856.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al.,<br>Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. <br><i>J Microbiol Methods</i>, <b>2014</b>. 100: p. 58-69.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/24614010.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P. and D. Naumann,<br>MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. <br><i>Proteomics, Glycomics and Antigenicity of BSL3 and BSL4 Agents, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.</i>, <b>2011</b>: p. 219-212.<br><br />
http://dx.doi.org/10.1002/9783527638192.ch17.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al.,<br>Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. <br><i>Anal Chem</i>, <b>2010</b>. 82(20): p. 8464-75.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/20866090.<br> &nbsp; <br></li><br />
<br />
<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al.,<br>Rapid identification of Burkholderia cepacia complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell MALDI-ToF mass spectrometry. <br><i>Analyst</i>, <b>2009</b>. 134(6): p. 1138-48.<br><br />
https://www.ncbi.nlm.nih.gov/pubmed/19475140.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al.,<br>Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. <br><i>Appl Environ Microbiol</i>, <b>2009</b>. 75(22): p. 7229-42.<br><br />
http://www.ncbi.nlm.nih.gov/pubmed/19767470.<br> &nbsp; <br></li><br />
<br />
<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al.,<br>MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. <br><i>Anal Chem</i>, <b>2008</b>. 80(6): p. 2026-34.<br> <br />
https://www.ncbi.nlm.nih.gov/pubmed/18290666.<br> &nbsp; <br></li><br />
<br />
</ol></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Download_MicrobeMS&diff=1222Download MicrobeMS2026-05-27T09:16:28Z<p>Laschp: /* Test Spectra and Test Databases */</p>
<hr />
<div>__TOC__<br />
<br />
MicrobeMS is available in two different editions: in (i) a stand-alone Windows 64-bit version requiring the Matlab Compiler Runtime (MCR) 64-bit and (ii) a Matlab pcode toolbox version requiring Matlab R2014 (Windows / Linux) or later.<br />
Please note that you accept with downloading the [[MicrobeMS_Wiki:General_disclaimer | license conditions]] of MicrobeMS.<br />
<br />
=== MicrobeMS stand-alone (Windows) ===<br />
<br />
<font color="red">'''NEW'''</font> - Microbe MS 0.93 stand-alone 64-bit version (Windows, May 2026). This version requires Matlab's MCR 2023b (64-bit, Windows). Please use the following link to download the installer for this edition of MicrobeMS<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.093.R2023b.setup.zip: '''microbems.v.093.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.92 stand-alone 64-bit version (Windows, January 2026). This version requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.092.R2023b.setup.zip: '''microbems.v.092.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.90d stand-alone 64-bit version (Windows, July 2025), requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.090d.R2023b.setup.zip: '''microbems.v.090d.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.89 stand-alone 64-bit version (Windows, February 2025), requires Matlab's MCR 2014a (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.089.R2014a.setup.zip: '''microbems.v.089.R2014a.setup.zip''']<br />
<br />
Microbe MS 0.84 stand-alone 64-bit version (Windows) from February 2022, requires Matlab's MCR 2014a.: <br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.084.setup.zip: '''microbems.v.084.setup.zip''']<br />
<br />
=== MicrobeMS pcode (Windows and Linux) ===<br />
<br />
<font color="red">'''NEW'''</font> - MicrobeMS version 0.92 Matlab pcode for Windows and Linux (January 2026):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v092.zip: '''pcode-microbems-v092.zip''']<br />
<br />
MicrobeMS version 0.87 Matlab pcode for Windows and Linux (June 2024):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v087.zip: '''pcode-microbems-v087.zip''']<br />
<br />
MicrobeMS version 0.82 Matlab pcode for Windows and Linux (December 2019):<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v082.zip: '''pcode-microbems-v082.zip''']<br />
<br />
=== Test Spectra and Test Databases ===<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (zip archive, Bruker Daltonics MS data format):<br />
<br />
[https://wiki.microbe-ms.com/uploads/ring-trial-RKI-spectra.zip '''ring-trial-RKI-spectra.zip''']<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''spec''), <br><br />
see also [[Data_Format_of_Spectral_Multifiles|Format of spectra multifiles (*.muf)]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-spectra.muf: '''RKI-ring-trial-spectra.muf''']<br />
<br />
Peak list files obtained from mass spectra of strains of the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''C''),<br><br />
see also [[Data_Format_of_Peak_List_Files|Format of peaks list files (*.pkf)]] to obtain information on the file format<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-test-data.pkf: '''RKI-ring-trial-test-data.pkf''']<br />
<br />
MS Excel template required to define and transfer metadata items into MicrobeMS,<br><br />
for file format information see [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/MALDI-Fields.xls: '''MALDI-Fields.xls''']<br />
<br />
Download the most actual database versions with microbial MALDI-ToF MS, or synthetic LC-MS libraries:<br />
<br />
[[Mass_Spectrometry_Databases |Further Mass Spectrometry Databases for MicrobeMS (''in silico'' and experimental databases, MALDI-ToF MS and LC-MS)]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Download_MicrobeMS&diff=1221Download MicrobeMS2026-05-27T09:15:31Z<p>Laschp: /* Test Spectra and Databases */</p>
<hr />
<div>__TOC__<br />
<br />
MicrobeMS is available in two different editions: in (i) a stand-alone Windows 64-bit version requiring the Matlab Compiler Runtime (MCR) 64-bit and (ii) a Matlab pcode toolbox version requiring Matlab R2014 (Windows / Linux) or later.<br />
Please note that you accept with downloading the [[MicrobeMS_Wiki:General_disclaimer | license conditions]] of MicrobeMS.<br />
<br />
=== MicrobeMS stand-alone (Windows) ===<br />
<br />
<font color="red">'''NEW'''</font> - Microbe MS 0.93 stand-alone 64-bit version (Windows, May 2026). This version requires Matlab's MCR 2023b (64-bit, Windows). Please use the following link to download the installer for this edition of MicrobeMS<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.093.R2023b.setup.zip: '''microbems.v.093.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.92 stand-alone 64-bit version (Windows, January 2026). This version requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.092.R2023b.setup.zip: '''microbems.v.092.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.90d stand-alone 64-bit version (Windows, July 2025), requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.090d.R2023b.setup.zip: '''microbems.v.090d.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.89 stand-alone 64-bit version (Windows, February 2025), requires Matlab's MCR 2014a (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.089.R2014a.setup.zip: '''microbems.v.089.R2014a.setup.zip''']<br />
<br />
Microbe MS 0.84 stand-alone 64-bit version (Windows) from February 2022, requires Matlab's MCR 2014a.: <br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.084.setup.zip: '''microbems.v.084.setup.zip''']<br />
<br />
=== MicrobeMS pcode (Windows and Linux) ===<br />
<br />
<font color="red">'''NEW'''</font> - MicrobeMS version 0.92 Matlab pcode for Windows and Linux (January 2026):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v092.zip: '''pcode-microbems-v092.zip''']<br />
<br />
MicrobeMS version 0.87 Matlab pcode for Windows and Linux (June 2024):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v087.zip: '''pcode-microbems-v087.zip''']<br />
<br />
MicrobeMS version 0.82 Matlab pcode for Windows and Linux (December 2019):<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v082.zip: '''pcode-microbems-v082.zip''']<br />
<br />
=== Test Spectra and Test Databases ===<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (zip archive, Bruker Daltonics MS data format):<br />
<br />
[https://wiki.microbe-ms.com/uploads/ring-trial-RKI-spectra.zip '''ring-trial-RKI-spectra.zip''']<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''spec''), <br><br />
see also [[Data_Format_of_Spectral_Multifiles|Format of spectra multifiles (*.muf)]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-spectra.muf: '''RKI-ring-trial-spectra.muf''']<br />
<br />
Peak list files obtained from mass spectra of strains of the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''C''),<br><br />
see also [[Data_Format_of_Peak_List_Files|Format of peaks list files (*.pkf)]] to obtain information on the file format<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-test-data.pkf: '''RKI-ring-trial-test-data.pkf''']<br />
<br />
MS Excel template required to define and transfer metadata items into MicrobeMS,<br><br />
for file format information see [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/MALDI-Fields.xls: '''MALDI-Fields.xls''']<br />
<br />
Download the most actual database versions with microbial MALDI-ToF MS, or synthetic LC-MS libraries:<br />
<br />
[[Mass_Spectrometry_Databases |Further Mass Spectrometry Databases for MicrobeMS (''in silico'' and experimental databases)]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Download_MicrobeMS&diff=1220Download MicrobeMS2026-05-27T09:15:06Z<p>Laschp: /* Test Spectra and Databases */</p>
<hr />
<div>__TOC__<br />
<br />
MicrobeMS is available in two different editions: in (i) a stand-alone Windows 64-bit version requiring the Matlab Compiler Runtime (MCR) 64-bit and (ii) a Matlab pcode toolbox version requiring Matlab R2014 (Windows / Linux) or later.<br />
Please note that you accept with downloading the [[MicrobeMS_Wiki:General_disclaimer | license conditions]] of MicrobeMS.<br />
<br />
=== MicrobeMS stand-alone (Windows) ===<br />
<br />
<font color="red">'''NEW'''</font> - Microbe MS 0.93 stand-alone 64-bit version (Windows, May 2026). This version requires Matlab's MCR 2023b (64-bit, Windows). Please use the following link to download the installer for this edition of MicrobeMS<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.093.R2023b.setup.zip: '''microbems.v.093.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.92 stand-alone 64-bit version (Windows, January 2026). This version requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.092.R2023b.setup.zip: '''microbems.v.092.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.90d stand-alone 64-bit version (Windows, July 2025), requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.090d.R2023b.setup.zip: '''microbems.v.090d.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.89 stand-alone 64-bit version (Windows, February 2025), requires Matlab's MCR 2014a (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.089.R2014a.setup.zip: '''microbems.v.089.R2014a.setup.zip''']<br />
<br />
Microbe MS 0.84 stand-alone 64-bit version (Windows) from February 2022, requires Matlab's MCR 2014a.: <br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.084.setup.zip: '''microbems.v.084.setup.zip''']<br />
<br />
=== MicrobeMS pcode (Windows and Linux) ===<br />
<br />
<font color="red">'''NEW'''</font> - MicrobeMS version 0.92 Matlab pcode for Windows and Linux (January 2026):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v092.zip: '''pcode-microbems-v092.zip''']<br />
<br />
MicrobeMS version 0.87 Matlab pcode for Windows and Linux (June 2024):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v087.zip: '''pcode-microbems-v087.zip''']<br />
<br />
MicrobeMS version 0.82 Matlab pcode for Windows and Linux (December 2019):<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v082.zip: '''pcode-microbems-v082.zip''']<br />
<br />
=== Test Spectra and Databases ===<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (zip archive, Bruker Daltonics MS data format):<br />
<br />
[https://wiki.microbe-ms.com/uploads/ring-trial-RKI-spectra.zip '''ring-trial-RKI-spectra.zip''']<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''spec''), <br><br />
see also [[Data_Format_of_Spectral_Multifiles|Format of spectra multifiles (*.muf)]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-spectra.muf: '''RKI-ring-trial-spectra.muf''']<br />
<br />
Peak list files obtained from mass spectra of strains of the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''C''),<br><br />
see also [[Data_Format_of_Peak_List_Files|Format of peaks list files (*.pkf)]] to obtain information on the file format<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-test-data.pkf: '''RKI-ring-trial-test-data.pkf''']<br />
<br />
MS Excel template required to define and transfer metadata items into MicrobeMS,<br><br />
for file format information see [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/MALDI-Fields.xls: '''MALDI-Fields.xls''']<br />
<br />
Download the most actual database versions with microbial MALDI-ToF MS, or synthetic LC-MS libraries:<br />
<br />
[[Mass_Spectrometry_Databases |Further Mass Spectrometry Databases for MicrobeMS (''in silico'' and experimental databases)]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1219Install MicrobeMS2026-05-27T09:12:17Z<p>Laschp: /* Install MicrobeMS as a stand-alone application (Windows) */</p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions, see table below for details). If the MCR is not installed, download for free the corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=800<br />
!width=300| MicrobeMS version<br />
!width=500| Matlab Componen Runtime (MCR) version<br />
|-<br />
| Version 0.93 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.92 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.90d (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.89 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows) - MCR 8.2<br />
|-<br />
| Version 0.84 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows) - MCR 8.2<br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1218Install MicrobeMS2026-05-27T09:10:54Z<p>Laschp: /* Install MicrobeMS as a stand-alone application (Windows) */</p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions). If the MCR is not installed, download the free corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=800<br />
!width=300| MicrobeMS version<br />
!width=500| Matlab Componen Runtime (MCR) version<br />
|-<br />
| Version 0.93 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.92 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.90d (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows) - MCR 23.2<br />
|-<br />
| Version 0.89 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows) - MCR 8.2<br />
|-<br />
| Version 0.84 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows) - MCR 8.2<br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1217Install MicrobeMS2026-05-27T09:08:42Z<p>Laschp: /* Install MicrobeMS as a stand-alone application (Windows) */</p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions). If the MCR is not installed, download the free corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=600<br />
!width=300| MicrobeMS version<br />
!width=300| Matlab Componen Runtime (MCR) version<br />
|-<br />
| Version 0.93 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.92 (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.90d (Windows stand-alone)<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.89 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows)<br />
|-<br />
| Version 0.84 (Windows stand-alone)<br />
| MCR 2014a (64-bit, Windows)<br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Download_MicrobeMS&diff=1216Download MicrobeMS2026-05-27T09:07:19Z<p>Laschp: /* MicrobeMS stand-alone (Windows) */</p>
<hr />
<div>__TOC__<br />
<br />
MicrobeMS is available in two different editions: in (i) a stand-alone Windows 64-bit version requiring the Matlab Compiler Runtime (MCR) 64-bit and (ii) a Matlab pcode toolbox version requiring Matlab R2014 (Windows / Linux) or later.<br />
Please note that you accept with downloading the [[MicrobeMS_Wiki:General_disclaimer | license conditions]] of MicrobeMS.<br />
<br />
=== MicrobeMS stand-alone (Windows) ===<br />
<br />
<font color="red">'''NEW'''</font> - Microbe MS 0.93 stand-alone 64-bit version (Windows, May 2026). This version requires Matlab's MCR 2023b (64-bit, Windows). Please use the following link to download the installer for this edition of MicrobeMS<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.093.R2023b.setup.zip: '''microbems.v.093.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.92 stand-alone 64-bit version (Windows, January 2026). This version requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.092.R2023b.setup.zip: '''microbems.v.092.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.90d stand-alone 64-bit version (Windows, July 2025), requires Matlab's MCR 2023b (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.090d.R2023b.setup.zip: '''microbems.v.090d.R2023b.setup.zip''']<br />
<br />
Microbe MS 0.89 stand-alone 64-bit version (Windows, February 2025), requires Matlab's MCR 2014a (64-bit, Windows).<br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.089.R2014a.setup.zip: '''microbems.v.089.R2014a.setup.zip''']<br />
<br />
Microbe MS 0.84 stand-alone 64-bit version (Windows) from February 2022, requires Matlab's MCR 2014a.: <br />
<br />
[https://wiki.microbe-ms.com/uploads/microbems.v.084.setup.zip: '''microbems.v.084.setup.zip''']<br />
<br />
=== MicrobeMS pcode (Windows and Linux) ===<br />
<br />
<font color="red">'''NEW'''</font> - MicrobeMS version 0.92 Matlab pcode for Windows and Linux (January 2026):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v092.zip: '''pcode-microbems-v092.zip''']<br />
<br />
MicrobeMS version 0.87 Matlab pcode for Windows and Linux (June 2024):<br />
requires Matlab R2022a or newer<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v087.zip: '''pcode-microbems-v087.zip''']<br />
<br />
MicrobeMS version 0.82 Matlab pcode for Windows and Linux (December 2019):<br />
<br />
[https://wiki.microbe-ms.com/uploads/pcode-microbems-v082.zip: '''pcode-microbems-v082.zip''']<br />
<br />
=== Test Spectra and Databases ===<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (zip archive, Bruker Daltonics MS data format):<br />
<br />
[https://wiki.microbe-ms.com/uploads/ring-trial-RKI-spectra.zip '''ring-trial-RKI-spectra.zip''']<br />
<br />
MALDI-ToF mass spectra from the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''spec''), <br><br />
see also [[Data_Format_of_Spectral_Multifiles|Format of spectra multifiles (*.muf)]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-spectra.muf: '''RKI-ring-trial-spectra.muf''']<br />
<br />
Peak list files obtained from mass spectra of strains of the ''so called'' [https://pubmed.ncbi.nlm.nih.gov/26063856/ '''RKI ring trial study'''] (Matlab data format, structure array ''C''),<br><br />
see also [[Data_Format_of_Peak_List_Files|Format of peaks list files (*.pkf)]] to obtain information on the file format<br />
<br />
[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-test-data.pkf: '''RKI-ring-trial-test-data.pkf''']<br />
<br />
MS Excel template required to define and transfer metadata items into MicrobeMS,<br><br />
for file format information see [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]]:<br />
<br />
[https://wiki.microbe-ms.com/uploads/MALDI-Fields.xls: '''MALDI-Fields.xls''']<br />
<br />
Download the most actual database versions with microbial MALDI-ToF MS, or synthetic LC-MS libraries:<br />
<br />
[[Mass_Spectrometry_Databases |Mass Spectrometry Databases from the RKI at ZENODO]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1215Install MicrobeMS2026-05-27T09:06:32Z<p>Laschp: /* Install MicrobeMS as a stand-alone application (Windows) */</p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions). If the MCR is not installed, download the free corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=600<br />
!width=200| MicrobeMS version<br />
!width=400| Matlab Componen Runtime (MCR) version<br />
|-<br />
| Version 0.93<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.92<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.90d<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.89<br />
| MCR 2014a (64-bit, Windows)<br />
|-<br />
| Version 0.84<br />
| MCR 2014a (64-bit, Windows)<br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1214Install MicrobeMS2026-05-27T09:05:40Z<p>Laschp: /* Install MicrobeMS as a stand-alone application (Windows) */</p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions). If the MCR is not installed, download the free corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=600<br />
!width=200| MicrobeMS version<br />
!width=400| Matlab Componen Runtime (MCR) version<br />
|-<br />
| Version 0.93<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.92<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.90d<br />
| MCR 2014a (64-bit, Windows)<br />
|-<br />
| Version 0.89<br />
| MCR 2014a (64-bit, Windows)<br />
|-<br />
| Version 0.84<br />
| MCR 2014a (64-bit, Windows)<br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Install_MicrobeMS&diff=1213Install MicrobeMS2026-05-27T09:03:11Z<p>Laschp: </p>
<hr />
<div>__FORCETOC__<br />
== Preface ==<br />
<br />
MicrobeMS is a Matlab-based application which can be installed in two different versions (with basically the same functionality):<br><br />
<br />
* The stand-alone version requires installation of the MATLAB Compiler Runtime (MCR R2014a, or MCR 2023b, Windows 64-bit).<br />
* MicrobeMS as a Matlab pcode toolbox. For this, a licensed version of Matlab will be required.<br />
<br />
== Install MicrobeMS as a stand-alone application (Windows) ==<br />
&nbsp;<br><br />
<br />
<b>Prerequisites</b> <br />
<br />
MicrobeMS is Matlab based, so please verify that the MATLAB Compiler Runtime (MCR) is installed (MCR R2014a [8.3], or MCR R2023b (23.2), 64-bit Windows versions). If the MCR is not installed, download the free corresponding Windows 64-bit version of the MCR from the MathWorks website by navigating to<br />
<br />
[https://www.mathworks.com/products/compiler/mcr/index.html https://www.mathworks.com/products/compiler/mcr/index.html]<br />
<br />
<span class="mw-headline" id="peak table format"></span><br />
<br><br />
{| class="wikitable" width=600<br />
!width=200| Fields<br />
!width=400| Description<br />
|-<br />
| Version 0.93<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.92<br />
| MCR 2023b (64-bit, Windows)<br />
|-<br />
| Version 0.90d<br />
| MCR 2014a (64-bit, Windows)<br />
|-<br />
| C.pik(4,:) <br> &nbsp; <br><br />
| in case of single spectra, i.e. no database or average spectra: baseline-corrected absolute intensities of the peaks, in case of average or database spectra: the relative peak frequency<br />
|-<br />
| C.pik(5,:) <br> &nbsp; <br><br />
| FWHH of the given peak (not always present, requires QT) <br> &nbsp; <br><br />
|-<br />
| C.pik(6,:) <br> &nbsp; <br><br />
| resolving power of the given peak (not always present, requires QT) <br> &nbsp; <br><br />
|}<br />
<br />
NOTE: To run the MCR installer administrator rights may be required. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
<br />
* Login with your Windows user account and download / unzip the MicrobeMS archive ''microbems.v.0XX.setup.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* Start ''microbems.v.0XX.setup.exe'' and follow the instructions of the installation routine. During setup, you may be asked for the Windows administrator password. <br />
<br />
* The license file ''genkey.gen'' is not included in the installation package. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]<br />
<br />
* In return, the license file ''genkey.gen'' will be emailed to you. Copy the license key into the logfile directory of MicrobeMS (version 0.82 and later, DiaryDir, ''C:\Users\Public\Documents\Matlab''). In case of MicrobeMS versions before v. 0.82 the license key must be copied into the HomeDir of MicrobeMS (''C:\Program files\MicrobeMS\MicrobeMS'', default). Note that in some instances administrator rights may be required.<br />
<br />
== Installation of MicrobeMS as a Matlab pcode toolbox (Windows / Linux) ==<br />
<br />
&nbsp;<br><br />
<b>Prerequisites</b> <br />
<br />
Please note that Matlab R2014a, or newer, Matlabs's Statistics and Bioinformatics toolboxes are required when using MicrobeMS as a pcode toolbox. A complete 64-bit system consisting of 64-bit hardware, a Windows, or LINUX 64-bit operating system and Matlab 64-bit are highly recommended. For further details please refer also to the section [[computer_specification|Specification of computer configuration]].<br />
<br />
&nbsp;<br><br />
<b>Installation</b> <br />
&nbsp;<br><br />
<br />
* Download and unzip the MicrobeMS toolbox archive ''pcode-microbems-v0XX.zip'' (see [[download_MicrobeMS|Downloading MicrobeMS]])<br />
<br />
* It is recommended to create an additional subdirectory ''mass'' in the directory ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or in the folder ''/home/YourUserName'' (LINUX). Copy then all files into the new folder.<br />
<br />
* Start Matlab and enter at the command prompt<br />
<br />
>> addpath(''FullPathToMass'');<br />
<br />
<ul> where suggested values for ''FullPathToMass'' are ''C:\Users\YourUserName\Documents\Matlab\mass'' (Windows) or ''/home/YourUserName/mass'' (LINUX). Of note, ''addpath'' does not permanently add ''FullPathToMass'' to Matlab's search folders, so the command must be called after every start of Matlab. See below to add ''FullPathToMass'' permanently .</ul><br><br />
<br />
* To run MicrobeMS call the file ''mass.p'' by typing<br />
<br />
>> mass;<br />
<br />
* The license file ''genkey.gen'' is not included in the pcode installation archive. To register for a free license please send an email with your name and the name of the institution you are working for to the address [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. In return, the license file ''genkey.gen'' will be emailed to you. <br />
<br />
* Copy the license key into one of the following logfile directories (''DiaryDir'') of MicrobeMS: ''C:\Users\YourUserName\Documents\Matlab'' (Windows) or ''/home/YourUserName'' (LINUX).<br />
<br />
* To add the the MicrobeMS toolbox path permanently in Matlab type <br />
<br />
>> edit pathdef;<br />
<br />
<ul> at the command prompt. The editor will now load the file ''pathdef.m''. Add a new line with the complete path ''FullPathToMass'' to the MicrobeMS pcode toolbox. Store ''pathdef.m'', close the editor and restart Matlab to apply the changes made. </ul><br />
<br />
See also<br />
<ul><br />
* [[How_to_Obtain_a_License|How to obtain the license key?]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[Publications_with_MicrobeMS|Acknowledgements, relevant publications]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
</ul></div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Wilcoxon_Rank-Sum_Tests&diff=1212Wilcoxon Rank-Sum Tests2026-05-01T15:54:39Z<p>Laschp: Created page with "__FORCETOC__ == Introduction == input parameters for univariate t-tests| [https://en.wikipedia.org/wiki/Mann-Whitney_U_test Wilcoxon rank-sum test] (Wikipedia) To be continued (2026) == Parameter of Wilcoxon rank-sum tests == * '''m/z range''': lower and upper bounds of the m/z region in which the series of Wilcoxon rank-sum tests are to be performed * '''&alpha;''': significance level of the Wilcoxon rank-sum tests * '''''dx''''' (ppm): a..."</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|input parameters for univariate t-tests|]]<br />
<br />
[https://en.wikipedia.org/wiki/Mann-Whitney_U_test Wilcoxon rank-sum test] (Wikipedia)<br />
<br />
To be continued (2026)<br />
<br />
== Parameter of Wilcoxon rank-sum tests ==<br />
<br />
* '''m/z range''': lower and upper bounds of the m/z region in which the series of Wilcoxon rank-sum tests are to be performed<br />
* '''&alpha;''': significance level of the Wilcoxon rank-sum tests<br />
* '''''dx''''' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* '''use intensities''': defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* '''show histogram''': shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing Wilcoxon rank-sum tests ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pulldown menu.<br />
<br />
2. Wilcoxon rank-sum tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pulldown menu.<br />
<br />
3. The test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing|spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the Wilcoxon rank-sum test function.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''peak frequency plots'' from the ''Analysis'' pulldown menu. Choose options ''from selection'' or ''from class X'' if the peak frequency test should involve selected spectra or spectra with an appropriate class labeling, respectively.<br />
<br />
== Output of Wilcoxon rank-sum test ==<br />
<br><br />
Example of the output from a series of Wilcoxon rank-sum tests taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:Wilcox-cmdln-output.png|576px|thumb|Command line output of Wilcoxon-tests. In this example, the m/z segment centered around 2518.0283 Th shows the greatest potential for distinguishing between classes I and II.]]<br />
|[[File:Wilcox-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by Wilcoxon rank-sum tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=MicrobeMS_-_A_Matlab_Toolbox_for_Microbial_Identification_Based_on_Mass_Spectrometry&diff=1211MicrobeMS - A Matlab Toolbox for Microbial Identification Based on Mass Spectrometry2026-05-01T15:53:59Z<p>Laschp: /* Identification of MS Biomarker Peaks */</p>
<hr />
<div>== Introduction ==<br />
<br />
The MicrobeMS software package is a program specifically designed for the analysis of MALDI-ToF mass spectra of microbial samples. The software was developed by [http://www.peter-lasch.de: Peter Lasch] at the [https://www.rki.de Robert-Koch-Institute (RKI)] in Berlin/Germany and can be used to identify microbial species based on their mass spectral patterns. The program is a comprehensive [http://www.mathworks.com: Matlab]-based package that operates under Windows 7/8/8.1/10/11 and LINUX (Debian, MicrobeMS versions later than 0.81). Original MALDI-ToF mass spectra in the format defined by [http://www.bdal.com: Bruker Daltonics] or by [http://www.shimadzu.com: Shimadzu] (via the mzXML data format) can be imported, processed and converted into a Matlab data matrix format specific to the MicrobeMS program.<br />
<br />
The software allows standard mass spectrometry manipulations such as smoothing, baseline correction, normalization, peak detection, auto-calibration to mention some preprocessing functions. Furthermore, functionalities of the software include, among others, microbial identification analysis based on spectral distances and machine learning methods (ML), e.g. by artificial neural networks (ANN) with visualization of the identification results, unsupervised hierarchical cluster analysis, biomarker analysis, pseudo-gel view generation, as well as microbial mass spectra database management including interfaces for organizing mass spectral metadata. Since the software also runs in a full Windows, or LINUX, 64-bit environment, the number of spectra in the data sets is limited only by the amount of available memory (RAM).<br><br />
<br />
== Getting Started ==<br />
* [[download_MicrobeMS|Downloading MicrobeMS]]<br />
* [[computer_specification|Specification of computer configuration]]<br />
* [[install_MicrobeMS|How to install MicrobeMS]]<br />
* [[Screenshot_of_MicrobeMS|Screenshot]] of the main gui<br />
* [[MicrobeMS_Wiki:General_disclaimer |License conditions]]<br />
* [[How_to_Obtain_a_License|How to obtain a license key]]<br />
* [[Mass Spectrometry Databases|Mass spectrometry databases]]<br />
* [[Publications_with_MicrobeMS|Publications with MicrobeMS, acknowledgements]]<br />
* [[Other MALDI-ToF MS Ressources|Other MALDI-ToF MS ressources]]<br />
* [[Frequently_Asked_Questions_(FAQ)|Frequently asked questions (FAQ)]]<br />
<br />
== Management of Metadata Information ==<br />
<br />
* [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]] (spectrum metadata, taxonomic information, culture conditions, sample preparation methods, etc.)<br />
<br />
== Description of Data File Formats Specific to MicrobeMS ==<br />
* [[Data_Format_of_Spectral_Multifiles|Description of the format of spectral multifiles (*.muf)]]<br />
* [[Data_Format_of_Peak_List_Files|Description of the format of peak list files (*.pkf)]]<br />
* Description of the format of quality test files (*.mat)<br />
<!-- commented out * [[Format_of_Quality_Test_Result_Files|Description of the format of quality test files (*.mat)]] --><br />
* [[Description of MicrobeMS' main parameter file 'microbems.opt'|Description of the parameter file ''microbems.opt'']]<br />
<br />
== Import and Export of Mass Spectra and Mass Spectral Libraries ==<br />
<br />
* [[Load spectra (Bruker format)|Load spectral data files]] acquired by Bruker Daltonics MALDI-ToF mass spectrometers<br />
* [[Data_Format_of_Spectral_Multifiles|Load / store spectral multifiles (*.muf)]]<br />
* [[Data_Format_of_Peak_List_Files|Load / store peak list files (*.pkf)]]<br />
* [[Store spectra (Bruker format)|Store spectra in a Bruker-specific data format]]<br />
* [[Export spectra to ASCII|Store spectra in a standard ASCII data format]] (export to ASCII)<br />
* [[Store spectra (NeuroDeveloper format)|Store peaklist data in a format specific to the NeuroDeveloper software]] (export to NeuroDeveloper)<br />
* [[Import Mass Spectra in a mzXML Data Format|Import mass spectral data from mzXML data]] (allows importing spectra from Shimadzu/bioMérieux systems)<br />
* [[Export XML Data|Export XML Data]] (required for identification analysis with MicrobeNet from the CDC)<br />
<br />
== Spectral Analysis and Visualization ==<br />
* [[Spectral Pre-processing|Spectral pre-processing]]: smoothing, baseline correction, normalization, cut, auto-calibration, reduce resolution (binning)<br />
* [[MALDI Quality Tests|Quality tests of MALDI-ToF mass spectra]]<br />
* [[Peak Detection|Peak detection]]<br />
* [[Averaging Mass Spectra|Averaging mass spectra]]<br />
* [[Display MS Metadata|Display spectral metadata]]<br />
* [[The Log-File (logfile.txt)|The log-file]] (logfile.txt)<br />
<br />
== Identification and Classification ==<br />
<br />
* [[Unsupervised Hierarchical Cluster Analysis|Unsupervised hierarchical cluster analysis]] <br />
* [[Create database spectra|Creating database spectra from individual microbial mass spectra]]<br />
* Compiling mass spectral databases<br />
* [[Microbial Identification based on Mass Spectral Libraries and Interspectral Distances|Microbial identification based on mass spectral libraries and interspectral distances]]<br />
<!--<br />
* [[Identification Analysis by Neural Networks|Identification analysis by artificial neural networks (ANN,]] requires the NeuroDeveloper software package from [http://www.synthon-analytics.de: Synthon Analytics]) --><br />
* Identification Analysis by Neural Networks, requires the NeuroDeveloper software package from [http://www.synthon-analytics.de: Synthon Analytics])<br />
* [[Identification Analysis by Means of LC-MS&sup1; and ''in silico'' Databases|Identification analysis by means of LC-MS&sup1; and ''in silico'' databases]]<br />
<br />
== Identification of MS Biomarker Peaks ==<br />
<br />
* Generation of [[Creating Pseudo-Gel Views|''pseudo-gel'' views]] from microbial mass spectra<br />
* [[Class Assignment|How to perform class assignments]]<br />
* [[Peak Frequency Test|Peak frequency tests]]<br />
* [[Two-samples t-Tests|Two-samples t-tests]]<br />
* [[Wilcoxon Rank-Sum Tests|Wilcoxon rank-sum tests]]</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1209Two-samples t-Tests2026-05-01T15:30:17Z<p>Laschp: /* Output of the univariate t-test series */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* '''m/z range''': lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* '''&alpha;''': significance level of the t-tests<br />
* '''''dx''''' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* '''use intensities''': defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* '''show histogram''': shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests. In this example, the m/z segment centered around 2518.0283 Th shows the greatest potential for distinguishing between classes I and II.]]<br />
|[[File:T-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by univariate t-tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1208Two-samples t-Tests2026-05-01T15:28:43Z<p>Laschp: /* Output of the univariate t-test series */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* '''m/z range''': lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* '''&alpha;''': significance level of the t-tests<br />
* '''''dx''''' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* '''use intensities''': defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* '''show histogram''': shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests. In the given example, the m/z segment centered around 2518.0283 Th has the highest potential to discriminate between classes I and II.]]<br />
|[[File:T-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by univariate t-tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1207Two-samples t-Tests2026-05-01T15:26:37Z<p>Laschp: /* Output of the univariate t-test series */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* '''m/z range''': lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* '''&alpha;''': significance level of the t-tests<br />
* '''''dx''''' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* '''use intensities''': defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* '''show histogram''': shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests. In the given example, the m/z segment centered around 2518.0283 Th has the highest potential to discriminate classes I and II.]]<br />
|[[File:T-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by univariate t-tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1206Two-samples t-Tests2026-05-01T15:20:10Z<p>Laschp: /* Parameter of two-samples t-tests */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* '''m/z range''': lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* '''&alpha;''': significance level of the t-tests<br />
* '''''dx''''' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* '''use intensities''': defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* '''show histogram''': shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by univariate t-tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1202Two-samples t-Tests2026-05-01T15:13:39Z<p>Laschp: /* Output of the univariate t-test series */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|Plot of p-values (log scaled) obtained by univariate t-tests using peak data from individual m/z segments as inputs: The smaller the p-value, the higher the discriminative potential of biomarker peaks at the specific m/z positions]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1201Two-samples t-Tests2026-05-01T14:58:19Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-sample t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-sample t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1200Two-samples t-Tests2026-05-01T14:56:51Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-samples t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1199Two-samples t-Tests2026-05-01T14:56:22Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-samples t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis <math forcemathmode="png">H_{(1)}</math> is that the peak intensity data come from populations with unequal means.<br />
<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1198Two-samples t-Tests2026-05-01T14:55:39Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs consecutively two-samples t-tests in segments of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of labeled MALDI-ToF mass spectra that exhibit certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis <math forcemathmode="png">H_{(0)}</math> that the peak intensity data in classes I and class II arise from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1197Two-samples t-Tests2026-05-01T14:50:26Z<p>Laschp: /* Parameter of two-samples t-tests */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the series of t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1194Two-samples t-Tests2026-05-01T13:45:40Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1193Two-samples t-Tests2026-05-01T13:45:16Z<p>Laschp: /* Introduction */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test | t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1191Two-samples t-Tests2026-05-01T13:44:18Z<p>Laschp: /* Parameter of the two-samples t-test */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test| t-test] (Wikipedia)<br />
<br />
== Parameter of two-samples t-tests ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1190Two-samples t-Tests2026-05-01T13:43:42Z<p>Laschp: /* Performing t-test series */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test| t-test] (Wikipedia)<br />
<br />
== Parameter of the two-samples t-test ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' &rarr; ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1188Two-samples t-Tests2026-05-01T13:42:14Z<p>Laschp: /* Parameter of the two-samples t-test */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test| t-test] (Wikipedia)<br />
<br />
== Parameter of the two-samples t-test ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th<br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' --> ''class X'' from the ''Edit'' pull down menu.<br />
<br />
3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
<br />
4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
<br />
5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
<br />
== Output of the univariate t-test series ==<br />
<br><br />
Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
<br />
{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschphttps://wiki-ms.microbe-ms.com/index.php?title=Two-samples_t-Tests&diff=1185Two-samples t-Tests2026-05-01T13:40:39Z<p>Laschp: /* Parameter of the two-samples t-test */</p>
<hr />
<div>__FORCETOC__<br />
== Introduction ==<br />
<br />
[[File:T-test.jpg|right|thumb|Input parameters for univariate t-tests]]<br />
<br />
This function performs two-samples t-tests in each segment of the MALDI-ToF mass spectra using peak table data as inputs. t-tests are useful for biomarker screening in ensembles of mass spectra exhibiting certain degree of similarity. A two-samples t-test in a given m/z segment returns a test decision for the null hypothesis H(0) that the peak intensity data in class I and class II come from independent random samples from normal distributions with equal means and variances. The alternative hypothesis H(1) is that the peak intensity data come from populations with unequal means.<br />
<br />
See also [https://en.wikipedia.org/wiki/Student's_t-test| t-test] (Wikipedia)<br />
<br />
== Parameter of the two-samples t-test ==<br />
<br />
* m/z range: lower and upper bounds of the m/z region in which the t-tests are to be performed<br />
* &alpha;: significance level of the t-tests<br />
* ''dx'' (ppm): a parameter defining the relative width and thus the number of the spectrum segments. A spectrum segment centered at position <math forcemathmode="png">{x_{i}}</math> covers a m/z interval of an absolute width equaling <math forcemathmode="png">width_{abs,x(i)} = {x_{i}*dx*10^{-6}}</math>. The lower and upper bounds of the spectrum segments are defined by <math forcemathmode="png">[{x_{i}*(1-dx)*0.5*10^{-6}}]</math> (lower bound) and <math forcemathmode="png">[{x_{i}*(1+dx)*0.5*10^{-6}}]</math> (upper bound), respectively. Consequently, a spectrum segment of width ''dx''=1000 and centered at <math forcemathmode="png">{x_{i}}</math>=2000 Th would be 2 Th wide with boundaries located at 1999 and 2001 Th. <br />
* intensity: defines if barcode spectra (checkbox unchecked) or peak weighting factors (checked) are utilized as test inputs.<br />
* show histogram: shows a histogram with test outputs (p-values, AUC, etc.) and provides also the mean, median and the standard deviation of the test variables.<br />
<br />
== Performing t-test series ==<br />
<br />
1. Load the mass spectral data files via the [[Load spectra (Bruker format)|load spectra]] (Bruker data file format), [[Import Mass Spectra in a mzXML Data Format|import spectra from mzXML data]], or the ''load MS multifile'' options of the ''File'' pull down menu.<br />
<br />
2. Two-samples t-tests are carried out from labeled spectra, i.e. from spectra with a [[Class Assignment|class assignment]]. To perform the test label two groups of spectra as class 1 and as class 2, respectively. Labeling, or class assignment, can be carried out by selecting the appropriate spectra and choosing ''class assignments'' --> ''class X'' from the ''Edit'' pull down menu.<br />
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3. The t-test routine always starts from original MALDI-ToF mass spectra, i.e. [[Spectral Pre-processing| spectral pre-processing]] and [[Peak Detection|peak detection]] is carried out automatically using pre-defined parameters. Existing pre-processed spectra and pre-defined peak tables are ignored by the test routine.<br />
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4. Define test parameter, such as &alpha; (significance level), the m/z range and dx (''ppm'') which has a default value of 1000 (relative, in ppm). The parameter dx defines the width of m/z segments in which spectra are divided during the test. Peaks found in the same m/z segment are considered identical while mass peaks in different segments are considered different peaks. <br />
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5. When finished select ''t-test'' from the ''Analysis'' pull down menu. Choose options ''plot decision for H(0)'', ''plot p-values'' or ''plot t-values'', to obtain the respective outputs of the t-tests.<br />
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== Output of the univariate t-test series ==<br />
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Example of the output from a serial t-test taken from the [[The Log-File (logfile.txt)|log file]] of MicrobeMS:<br />
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{|<br />
|- style="vertical-align:top;"<br />
|[[File:t-test-cmdln-output.png|474px|thumb|Command line output of t-tests]]<br />
|[[File:T-test-plot.jpg|400px|thumb|p-values plot of univariate t-tests (log scaled): The smaller the p-value at the specific m/z position the higher the discriminative potential of biomarker peaks at this position]]<br />
|}</div>Laschp