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ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)

Authors: Lasch, Peter; Stämmler, Maren; Schneider, Andy;

Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)

Abstract

(Version 20181130) Edit #1 (Mar 06, 2023): New database version (v.4.2 - 20230306) - available: https://zenodo.org/records/14562231 Version 3 (20181130) of the RKI’s MALDI-TOF mass spectral database represents the second update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI database v.3 contains altogether 6264 mass spectra from 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 the diagnostics 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 its native data format (Bruker Daltonics). Please refer to the pdf file (181130-ZENODO-Metadata.pdf) to obtain information on cultivation condition, sample preparation and details of spectra acquisition. Do not try to print this document (~1000 pages!) The pkf-file (181130_ZENODO_Peaklist_30Peaks_1.6.pkf) contains the MS peak list data in a Matlab compatible format. The latter data file can be imported into MicrobeMS, a Matlab-based free-of-charge software solution developed at RKI. MicrobeMS is available from https://wiki-ms.microbe-ms.com. The RKI mass spectral database will be updated on a regular basis. The author's grateful thanks are given to the following persons for providing microbial strains and species, or mass spectra. Without their help this work would not be possible. Wolfgang Beyer - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany Guido Werner - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany Alejandra Bosch - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina Michal Drevinek - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic Roland Grunow - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany Daniela Jacob - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany Silke Klee - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany Jörg Rau - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany Jens Jacob - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany Martin Mielke - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany Monika Ehling-Schulz - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria Armand Paauw - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands

License type for data base files (spectra): Creative Commons Attribution Non Commercial 4.0 International (CC-BY-NC): Licensees must credit the original authors by stating their names & the original work's title. Licensees may copy, distribute, display, and perform the work and make derivative works and remixes based on it only for non-commercial purposes.

Keywords

Identification, Spectral Database, MALDI TOF Mass Spectrometry

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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