== Acknowledgements ==

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)]).

We kindly ask users of MicrobeMS and MicrobeMS data sets to acknowledge us in their publications by citing the following publication:

<ul><ul>
{| class="wikitable" width=800
|-
| [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
|}
  
</ul></ul>

== Publications in which MicrobeMS has been used or mentioned ==
  <ol start="34" reversed="reversed">

<li>Lasch, P. MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry. bioRxiv, 2026. (preprint), not peer reviewed) .
https://doi.org/10.64898/2026.05.08.723807   </li>

<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. Mol Cell Proteomics, 2026. 101539. 
https://doi.org/10.1016/j.mcpro.2026.101539   </li>

<li>Lasch, P., W. Beyer, A. Bosch, R. Borriss, et al., A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. Sci Data, 2025. 12(1): p. 187. 
https://doi.org/10.1038/s41597-025-04504-z   </li>

<li>Blumenscheit, C., Pfeifer, Y., Werner, G., John, C., Layer-Nicolaou, F., Schneider, A., Lasch, P., Doellinger, J., Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics. bioRxiv - biorxiv.org, Oct 28, 2024. 
https://doi.org/10.1101/2024.09.09.612008 .   </li>

<li>Collins, T.J., Muste, C., Owens, K.G. Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data. J Am Soc Mass Spectrom, 2024. 35, 6, 1352–1362. 
https://dx.doi.org/10.1021/jasms.4c00101.   </li>

<li>Lasch, P., M. Stämmler, and A. Schneider, 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). Zenodo, Mar 6, 2023. 
https://doi.org/10.5281/zenodo.7702374.   </li>

<li>Yang Y., Chapter 3: MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development. Detection and Analysis of Microorganisms by Mass Spectrometry: New Developments in Mass Spectrometry,06 Oct 2023 
https://doi.org/10.1039/BK9781837670338-00045.   </li>

<li> Yin, M. M. Gao, Y. L., Zheng, C.H., Liu, J.X., NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes. IEEE J Biomed Health Inform 27(3),1644-1653). 07 Feb. 2023. 
https://doi.org/10.1109/JBHI.2022.3229473.   </li>

<li> Wang, L., Yang, X., Kuang, L., Zhang, Z., Zeng, B., Chen, Z., Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations. Current Bioinformatics. 18(6):497-508(12). 2023. 
https://doi.org/10.2174/1574893618666230316113621.   </li>

<li>Ramadan, A.A., Bacterial typing methods from past to present: A comprehensive overview. Gene Reports, 2022. 29: p. 101675. 
https://dx.doi.org/10.1016/j.genrep.2022.101675.   </li>

<li>Qiao, L., MALDI-TOF MS for pathogenic bacteria analysis. International Journal of Mass Spectrometry, 2022. 482: p. 116935. 
https://dx.doi.org/10.1016/j.ijms.2022.116935.   </li>

<li>Blumenscheit, C., Y. Pfeifer, G. Werner, C. John, et al., Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics. Anal Chem, 2021. 93(44): p. 14599-14608. 
https://www.ncbi.nlm.nih.gov/pubmed/34697938.   </li>

<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger, Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS(1)) and in Silico Peptide Mass Libraries. Mol Cell Proteomics, 2020. 19(12): p. 2125-2139. 
https://www.ncbi.nlm.nih.gov/pubmed/32998977.   </li>

<li>Gittens, R.A., A. Almanza, K.L. Bennett, L.C. Mejia, et al., Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library. PLoS Negl Trop Dis, 2020. 14(10): p. e0008849. 
https://www.ncbi.nlm.nih.gov/pubmed/33108372.   </li>

<li>Baumeister, T.U.H., M. Vallet, F. Kaftan, L. Guillou, et al., Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry. Metabolomics, 2020. 16(3): p. 28. 
https://www.ncbi.nlm.nih.gov/pubmed/32090296.   </li>

<li>Lasch, P., A. Schneider, C. Blumenscheit, and J. Doellinger, Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data. bioRxiv, 2019. 
https://doi.dx.org/10.1101/870089 .   </li>

<li>Martina, P., M. Leguizamon, C.I. Prieto, S.A. Sousa, et al., Burkholderia puraquae sp. nov., a novel species of the Burkholderia cepacia complex isolated from hospital settings and agricultural soils. Int J Syst Evol Microbiol, 2018. 68(1): p. 14-20. 
https://www.ncbi.nlm.nih.gov/pubmed/29095137.   </li>

<li>Lasch, P., M. Stämmler, and A. Schneider, Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). Zenodo, 2018. November 30, 2018. 
https://dx.doi.org/10.5281/zenodo.1880975.   </li>

<li>Yang, Y., Y. Lin, Z. Chen, T. Gong, et al., Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment. Anal Chem, 2017. 89(22): p. 12556-12561. 
https://www.ncbi.nlm.nih.gov/pubmed/29086558.   </li>

<li>Meyer, B., A. Rabenstein, and J. Kuever, Mass Spectrometry: A Powerful Tool for the Identification of Wine-Related Bacteria and Yeasts. Biology of Microorganisms on Grapes, in Must and in Wine, 2017: p. 659-701. 
https://dx.doi.org/10.1007/978-3-319-60021-5_27.   </li>

<li>Lasch, P., M. Stämmler, and A. Schneider, Version 2 (20170523) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). Zenodo, 2017. May 23, 2017. 
http://dx.doi.org/10.5281/zenodo.582602.   </li>

<li>Lasch, P., M. Stämmler, and A. Schneider, A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI). Zenodo, 2016. Oct 27, 2017. 
http://dx.doi.org/10.5281/zenodo.163517.   </li>

<li>Lasch, P., D. Jacob, S.R. Klee, and G. Werner, Discriminatory Power of MALDI-TOF Mass Spectrometry for Phylogenetically Closely Related Microbial Strains. In: Applications of Mass Spectrometry in Microbiology, Plamen Demirev, Todd R. Sandrin (Eds.), 2016. Springer International Publishing: p. 203-234. 
http://dx.doi.org/10.1007/978-3-319-26070-9_8.   </li>

<li>Lasch, P., D. Jacob, R. Grunow, T. Schwecke, and J. Doellinger, Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS for the identification of highly pathogenic bacteria. TrAC Trends in Analytical Chemistry, 2016. 85, Part B(Trends in CBRN Measurements for safety and security): p. 103–111. 
http://dx.doi.org/10.1016/j.trac.2016.04.013.   </li>

<li>Lasch, P., R. Grunow, K. Antonation, S.A. Weller, and D. Jacob, Inactivation Techniques for MALDI-TOF MS Analysis of Highly Pathogenic Bacteria - A Critical Review. TrAC Trends in Analytical Chemistry, 2016. 85, Part B(Trends in CBRN Measurements for safety and security): p. 112–119. 
http://dx.doi.org/10.1016/j.trac.2016.04.012.   </li>

<li>Dieckmann, R., J.A. Hammerl, H. Hahmann, A. Wicke, et al., Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy. Faraday Discuss, 2016. 187: p. 353-75. 
http://www.ncbi.nlm.nih.gov/pubmed/27053001.   </li>

<li>Sousa, C., J. Botelho, F. Grosso, L. Silva, et al., Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions. Front Microbiol, 2015. 6: p. 481. 
http://www.ncbi.nlm.nih.gov/pubmed/26042113.   </li>

<li>Smith, E.M., Z.N. Willis, M. Blakeley, F. Lovatt, et al., Bacterial species and their associations with acute and chronic mastitis in suckler ewes. J Dairy Sci, 2015. 98(10): p. 7025-33. 
https://www.ncbi.nlm.nih.gov/pubmed/26277313.   </li>

<li>Lasch, P., T. Wahab, S. Weil, B. Palyi, et al., Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial. J Clin Microbiol, 2015. 53(8): p. 2632-40. 
https://www.ncbi.nlm.nih.gov/pubmed/26063856.   </li>

<li>Lasch, P., C. Fleige, M. Stammler, F. Layer, et al., Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. J Microbiol Methods, 2014. 100: p. 58-69. 
https://www.ncbi.nlm.nih.gov/pubmed/24614010.   </li>

<li>Lasch, P. and D. Naumann, MALDI-TOF Mass Spectrometry for the Rapid Identification of Highly Pathogenic Microorganisms. 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., 2011: p. 219-212. 
http://dx.doi.org/10.1002/9783527638192.ch17.   </li>

<li>Lasch, P., M. Drevinek, H. Nattermann, R. Grunow, et al., Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. Anal Chem, 2010. 82(20): p. 8464-75. 
http://www.ncbi.nlm.nih.gov/pubmed/20866090.   </li>

<li>Minan, A., A. Bosch, P. Lasch, M. Stammler, et al., 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. Analyst, 2009. 134(6): p. 1138-48. 
https://www.ncbi.nlm.nih.gov/pubmed/19475140.   </li>

<li>Lasch, P., W. Beyer, H. Nattermann, M. Stammler, et al., Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. Appl Environ Microbiol, 2009. 75(22): p. 7229-42. 
http://www.ncbi.nlm.nih.gov/pubmed/19767470.   </li>

<li>Lasch, P., H. Nattermann, M. Erhard, M. Stammler, et al., MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. Anal Chem, 2008. 80(6): p. 2026-34. 
https://www.ncbi.nlm.nih.gov/pubmed/18290666.   </li>

</ol>

Download MicrobeMS

2026-05-27T09:16:28Z

Laschp: /* Test Spectra and Test Databases */

__TOC__

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.
Please note that you accept with downloading the [[MicrobeMS_Wiki:General_disclaimer | license conditions]] of MicrobeMS.

=== MicrobeMS stand-alone (Windows) ===

'''NEW''' - 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

[https://wiki.microbe-ms.com/uploads/microbems.v.093.R2023b.setup.zip: '''microbems.v.093.R2023b.setup.zip''']

Microbe MS 0.92 stand-alone 64-bit version (Windows, January 2026). This version requires Matlab's MCR 2023b (64-bit, Windows).

[https://wiki.microbe-ms.com/uploads/microbems.v.092.R2023b.setup.zip: '''microbems.v.092.R2023b.setup.zip''']

Microbe MS 0.90d stand-alone 64-bit version (Windows, July 2025), requires Matlab's MCR 2023b (64-bit, Windows).

[https://wiki.microbe-ms.com/uploads/microbems.v.090d.R2023b.setup.zip: '''microbems.v.090d.R2023b.setup.zip''']

Microbe MS 0.89 stand-alone 64-bit version (Windows, February 2025), requires Matlab's MCR 2014a (64-bit, Windows).

[https://wiki.microbe-ms.com/uploads/microbems.v.089.R2014a.setup.zip: '''microbems.v.089.R2014a.setup.zip''']

Microbe MS 0.84 stand-alone 64-bit version (Windows) from February 2022, requires Matlab's MCR 2014a.:

[https://wiki.microbe-ms.com/uploads/microbems.v.084.setup.zip: '''microbems.v.084.setup.zip''']

=== MicrobeMS pcode (Windows and Linux) ===

'''NEW''' - MicrobeMS version 0.92 Matlab pcode for Windows and Linux (January 2026):
requires Matlab R2022a or newer

[https://wiki.microbe-ms.com/uploads/pcode-microbems-v092.zip: '''pcode-microbems-v092.zip''']

MicrobeMS version 0.87 Matlab pcode for Windows and Linux (June 2024):
requires Matlab R2022a or newer

[https://wiki.microbe-ms.com/uploads/pcode-microbems-v087.zip: '''pcode-microbems-v087.zip''']

MicrobeMS version 0.82 Matlab pcode for Windows and Linux (December 2019):

[https://wiki.microbe-ms.com/uploads/pcode-microbems-v082.zip: '''pcode-microbems-v082.zip''']

=== Test Spectra and Test Databases ===

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):

[https://wiki.microbe-ms.com/uploads/ring-trial-RKI-spectra.zip '''ring-trial-RKI-spectra.zip''']

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''), 
see also [[Data_Format_of_Spectral_Multifiles|Format of spectra multifiles (*.muf)]]:

[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-spectra.muf: '''RKI-ring-trial-spectra.muf''']

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''), 
see also [[Data_Format_of_Peak_List_Files|Format of peaks list files (*.pkf)]] to obtain information on the file format

[https://wiki.microbe-ms.com/uploads/RKI-ring-trial-test-data.pkf: '''RKI-ring-trial-test-data.pkf''']

MS Excel template required to define and transfer metadata items into MicrobeMS, 
for file format information see [[Adding / Editing Metadata of MALDI-TOF Mass Spectral Data|Adding / editing metadata of MS data files]]:

[https://wiki.microbe-ms.com/uploads/MALDI-Fields.xls: '''MALDI-Fields.xls''']

Download the most actual database versions with microbial MALDI-ToF MS, or synthetic LC-MS libraries:

[[Mass_Spectrometry_Databases |Further Mass Spectrometry Databases for MicrobeMS (''in silico'' and experimental databases, MALDI-ToF MS and LC-MS)]]

Laschp: /* What is the most actual version of MicrobeMS? */

== What is MicrobeMS? ==

The MicrobeMS software package is a program initially designed for the analysis of MALDI-ToF mass spectra of microbial samples. The MicrobeMS software was developed by Peter Lasch at the [http://www.rki.de/EN/Home/homepage_node.html: Robert-Koch-Institute (RKI)] in Berlin, Germany, and can be used to identify microbial strains and species based on their mass spectral patterns (MALDI-ToF MS and LC-MS).

== What is the most actual version of MicrobeMS? ==

The most actual version of the MicrobeMS software is MicrobeMS v. 0.93 from May 2026. The MicrobeMS software is continuously being developed further and can be downloaded [[download_MicrobeMS|here]].

'''May. 2026''': MicrobeMS v.0.93 released

* Minor edits: peak labels, error reporting, functionality of button ''more'' in function ''cmpr'', increased accuracy of peak detection, etc.
* Many code improvements & bug fixes

'''Jan. 2026''': MicrobeMS v.0.92

* [[Export XML Data|''XML export filter'']] for exporting MALDI-ToF MS spectrum data in to a format suitable for utilization of the MicrobeNet solution of the CDC's.
* New functionalities of the software function [[Microbial Identification based on Mass Spectral Libraries and Interspectral Distances|''Microbial identification based on mass spectral libraries and interspectral distances'']]: introducing a MBT like identification method
* [[MALDI Quality Tests|Quality test]] function significantly revised
* Many code improvements & bug fixes

'''Jun. 2025''': MicrobeMS v.0.90

* Major revisions of the function [[Unsupervised Hierarchical Cluster Analysis|''Unsupervised hierarchical cluster analysis'']]
* Code improvements and bug fixes

'''Feb. 2025''': MicrobeMS v.0.89

* Spectral prep-processing functions revised, see [https://wiki-ms.microbe-ms.com/index.php?title=Spectral_Pre-processing ''MicrobeMS - Spectral Pre-processing''] for details
* New function [https://wiki-ms.microbe-ms.com/index.php?title=MALDI_Quality_Tests ''Quality tests''] introduced
* Code improvements and bug fixes

'''Nov. 2019''': New functions and bug fixes of version 0.82:

* Further optimization of the function ''[[Microbial Identification based on Mass Spectral Libraries and Interspectral Distances|Microbial identification based on mass spectral libraries and interspectral distances]]'': Speed improvement by a factor of ~2 compared with version 0.80.
* MicrobeMS v. 0.82 allows analysis of LC-MS¹ spectra from microorganisms. Identification analysis of the LC-MS¹ data requires a synthetic ''in silico'' database of peptide mass data calculated from microbial genomes (see see '''Preprint''' Lasch P, Schneider A, Blumenscheit C and Doellinger J, [https://doi.org/10.1101/870089 ''Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹) and in silico Peptide Mass Data]'', bioRxiv (Dec 10, '''2018'''), doi:10.1101/870089.)
* Code improvements and bug fixes

'''Feb. 2017''': New functions and bug fixes of version 0.80 (selection):

* Re-designed interface to the NeuroDeveloper, a software for spectral analysis by artificial neural networks
* The import filter for binary Bruker Daltonics spectra files has been completely revised. This filter supports now also importing of MS data calibrated with the function ''cubic enhanced''.
* New design of the function ''[[Microbial Identification based on Mass Spectral Libraries and Interspectral Distances|Microbial identification based on mass spectral libraries and interspectral distances]]'': Matlab code for calculation of inter-spectral distances is now fully vectorized => speed improvement by a factor of ~4.
* Enhanced functionality of the option ''vary calibration parameters''
* Many code improvements & bug fixes.

== Is there a standalone version of MicrobeMS? ==

Yes, a standalone version of the MicrobeMS toolbox has been available since November 2016. The MicrobeMS standalone runs on Windows 7/8/8.1/10 and 11. Like previous pcode versions, the stand-alone version of MicrobeMS is based on Matlab, so the free Matlab Compiler Runtime environment is required. Please see [[install_MicrobeMS|How to install MicrobeMS]] and [[download_MicrobeMS|Downloading MicrobeMS]] for more details.

== MicrobeMS for Linux. Can MicrobeMS be used under Linux? ==

The first Linux version of MicrobeMS has been released in November 2019. This, and later software versions were tested under Matlab / Debian versions 10 and 11 and are available as a Matlab pcode toolboxes, i.e. requires Matlab plus two Matlab toolboxes (Statistics and Bioinformatics Toolboxes). More information can be found [[install_MicrobeMS|here]]. 
Update Oct 2024: A compiled Linux version (0.88) is available on request. Note that this version requires installation of the Matlab Compiler Runtime (MCR) R2020a.

== What is the Matlab Compiler Runtime (MCR) and why do I need to install it? ==

MicrobeMS is Matlab-based software. The Matlab component runtime is a standalone set of shared libraries that enables the execution of compiled Matlab applications or components on computers that do not have Matlab installed. The Matlab Compiler Runtime (MCR) is available at no charge from the Mathworks Web site. It is important that the version of MCR that runs the application on the target computer is compatible with the version of the Matlab compiler used (i.e. '''MCR 2014a Windows 64-bit''' must be installed to use version 0.84 of MicrobeMS [v. 0.82]). Later versions can be installed either as compatible with '''MCR 2014a Windows 64-bit''' with ''microbems.v.089.R2014a.setup.zip'' or '''MCR 2023b Windows 64-bit''' using ''microbems.v.089.R2023b.setup.zip''.

== Why I do need to install the MCR 2014a 64-bit and not a more recent version? ==
 
''This question has now been resolved, as a version for MCR 2023b is available since March 2025.'' 
 
First of all, MicrobeMS for Windows has been compiled with the Matlab compiler of Matlab version R2014a (Windows 64-bit). But why was a more recent compiler version not used?
The answer to this question has two parts: 
First, new Matlab versions usually introduce new features, while existing features may be modified or even removed. These changes require extensive source code compatibility checks when migrating to a newer Matlab / Matlab compiler version. Second, with the R2014b release, The Mathworks introduced major changes to the underlying graphics libraries, see https://de.mathworks.com/help/matlab/graphics_transition/major-graphics-changes-in-r2014b.html for details. I noticed that these changes caused performance problems on some computers, see https://de.mathworks.com/matlabcentral/answers/203918-slow-plotting-performance-starting-from-matlab-r2014b for an example. 
With this in mind, I decided to wait for the performance problems to be solved by hardware evolution, i.e. to wait for faster hardware to solve the problem by itself.

== I have installed MicrobeMS, but I would like to use also spectral databases ==

Databases of microbial mass spectra are not provided with the software (except some test spectra of selected ''Escherichia coli'' strains). For a limited number of users we provide spectral databases from our ''Yersinia'' and ''Bacillus'' study on personal request (signing of a data transfer agreement - DTA - is required). Please send an email with your request to the following address: [mailto:lasch@microbe-ms.com lasch@microbe-ms.com]. 

'''UPDATE Jan 2026''': An ''in silico'' MALDI-ToF MS database is available (CC-BY-NC-SA license). This database has been derived from UniprotKB proteomics databases, i.e. ultimately from microbial genomes. This database is still highly experimental and should not be used for diagnostic purposes. More details will follow in the near future.

'''UPDATE May 2023''': MALDI-ToF MS database version 4 is available, again under the Creatives Commons license (CC-BY-NC-SA). The RKI Database v.4 now contains a total of 11055 MALDI-ToF mass spectra from 1599 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as ''Bacillus anthracis'', ''Brucella melitensis'', ''Yersinia pestis'', ''Burkholderia mallei'' / ''pseudomallei'' and ''Francisella tularensis'' as well as a selection of spectra of their close and distant relatives, for details see https://doi.org/10.5281/zenodo.7702375.

'''UPDATE Dec 2019''': The concept of microbial identification by means of MALDI-ToF mass spectrometry measurements of cultivated microbial cells and comparison with entries of a spectral library has been adapted for LC-MS¹ based microbial identification, see '''Preprint''' Lasch P, Schneider A, Blumenscheit C and Doellinger J, [https://doi.org/10.1101/870089 ''Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹) and in silico Peptide Mass Data]'', bioRxiv (Dec 10, '''2018'''), doi:10.1101/870089. 
The respective ''in silico'' database can be downloaded from Zenodo (1.5 GB!): [https://doi.org/10.5281/zenodo.3573996 ''In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹)''] Zenodo, December 13, 2019. (cf. also [[Identification Analysis by Means of LC-MS¹ and ''in silico'' Databases|Identification analysis by means of LC-MS¹ and ''in silico'' databases]]).

'''UPDATE Nov 2018''': The third update of the RKI MALDI-ToF MS database is available (Creatives Commons license, CC-BY-NC-SA).
The RKI database v.3 contains a total of 6264 mass spectra of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as ''Bacillus anthracis'', ''Yersinia pestis'', ''Burkholderia mallei'', ''Burkholderia pseudomallei'' and ''Francisella tularensis'' as well as a selection of spectra from their close and more distant relatives. The database can be used as a reference for diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. Spectral data are distributed as a 7-zip archive that contains the original mass spectra in the original data format (Bruker Daltonics), see [https://doi.org/10.5281/zenodo.1880975 https://doi.org/10.5281/zenodo.1880975] for details.

'''UPDATE May 2017''': The second version of the RKI MALDI-ToF MS database is available (Creatives Commons license, CC-BY-NC-SA), see [http://doi.org/10.5281/zenodo.582602 http://doi.org/10.5281/zenodo.582602] for details.

'''UPDATE Oct 2016''': Since October 27, 2016 the database from the Robert Koch Institute (RKI) with MALDI-ToF mass spectra from the genera ''Bacillus'', ''Yersinia'' and ''Burkholderia'' is available for download (Zenodo, Creatives Commons license, CC-BY-NC-SA). The RKI database currently comprises 5531 spectra in the Bruker data format and the respective peak list file (*.pkf data format) allowing microbial identification using MicrobeMS version 0.80 and later, see the publication [http://doi.org/10.5281/zenodo.163517 ''A MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch Institute (RKI)''].

== What is a database spectrum? ==

A database (db) spectrum is a spectrum created from individual mass spectra, ideally containing the spectral variances of biological and technical replicate spectra. Spectra used to construct a database spectrum are ideally collected from identical or at least taxonomically closely related microbial strains. Database spectra are created from pre-processed spectra and their peak tables using standardized parameters (see the description of [[Create database spectra|database spectra]] for more details).