Publications with MicrobeMS

Acknowledgements

MicrobeMS is a software project that has been developed by Peter Lasch at the Proteomics and Spectroscopy unit at the 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 (e-mail)).

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

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

 

Publications in which MicrobeMS has been used or mentioned

 

37. Lasch, P.
    MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry.
    bioRxiv - biorxiv.org, May 12, 2026.
    https://doi.org/10.64898/2026.05.08.723807
 

38. 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
 

39. 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
 

40. 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 .
 

41. 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.
 

42. 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.
 

43. 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.
 

44. 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.
 

45. 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.
 

46. 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.
 

47. 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.
 

48. 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.
 

49. 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.
 

50. 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.
 

51. 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.
 

52. Mulner, P., Schwarz, E., Dietel, K., Junge, H., Herfort, S., Weydmann, M., Lasch, P., Cernava, T., Berg, G., Vater, J.
    Profiling for Bioactive Peptides and Volatiles of Plant Growth Promoting Strains of the Bacillus subtilis Complex of Industrial Relevance.
    Frontiers in Microbiology, 2020 11(1432): p. 1432.
    https://www.ncbi.nlm.nih.gov/pubmed/32695084
 

53. 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 - biorxiv.org, Dec 10, 2019.
    https://doi.org/10.1101/870089
 

54. 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.
 

55. 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.
 

56. Sauget, M., Valot, B., Bertrand, X., Hocquet, D.
    Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?
    Trends in Microbiology 2017 25(6): 447–455.
    https://www.ncbi.nlm.nih.gov/pubmed/28094091
 

57. 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.
 

58. 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.
 

59. 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.
 

60. 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.
 

61. 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.
 

62. 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.
 

63. 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.
 

64. 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.
 

65. 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.
 

66. 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.
 

67. 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.
 

68. 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.
 

69. 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.
 

70. 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.
 

71. 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.
 

72. 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.
 

73. 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.