As it is quite cumbersome to label many samples from within Bruker flexControl, we have created an easy method for naming samples using an Excel®/OpenOffice™ template.
If you haven't already, download the Excel template for renaming raw data files here. This template was designed to work with MALDI plates of up to 384 spots.
If you don’t have access to Microsoft Excel, we have successfully tested this with the free Excel alternative: Apache OpenOffice™ “Calc”, which can be found at www.openoffice.org. When saving the file, ensure you save it as type “Microsoft Excel 97/2000/XP (.xls)”.
The MALDI plate should be properly cleaned before use. In order to clean the MALDI plate, use the steps below: method adapted from Freiwald & Sauer
Notes: - Use fresh matrix solution and store unused solid CHCA between 2-8 °C. - There are many MALDI matrix alternatives. We have had success using CHCA, which is also more common for protein profiling of bacteria. It may be worth trying CHCA + DHB. Matrix selection depends on individual user needs. - For alternative small-molecule matrices please see: https://doi.org/10.1055/s-0042-104800
| | | | ------------- | ------------- | | 1. Apply bacteria directly without any prior chemical treatment. Smear a single bacterial colony in a thin layer directly onto the MALDI target plate using a sterile toothpick. | | | 2. Add 1 µl of PepMix and 1ul BTS to their respective external calibration spots. | | | 3. Add 1 µL of 70% formic acid to the spot, let air dry. | | | 4. Add 1 µL of MALDI matrix to every spot, let air dry. | |
Note: - For alternative sample preparation methods please refer to Freiwald & Sauer
| | | | ------------- | ------------- | | 1. Insert MALDI plate into the mass spectrometer | | | 2. Select the appropriate IDBac Method
A good overview of clustering methods may be found here
IDBac users are expected to use their own judgement and formal training in statistics to construct and validate their experiments. We also suggest users be familiar with the concepts covered in Section "6.4.3 Remarks on Statistical Problems with MS Data" from Bruker Daltonic's "ClinProTools User Manual".
Below are the some of the relevant R packages that we utilized within IDBac’s code. Without these authors’ hard work, this project would not be possible. - R Core - R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. - KNIME - Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., … Wiswedel, B. (2007). KNIME: The {K}onstanz {I}nformation {M}iner. In Data Analysis, Machine Learning and Applications:Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburgand (pp. 319–326). Springer. - MALDIquant - S. Gibb and K. Strimmer. 2012. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28, 2270-2271. - MALDIquantForeign - Sebastian Gibb (2015). MALDIquantForeign: Import/Export Routines for MALDIquant. R package version 0.10. https://CRAN.R-project.org/package=MALDIquantForeign - Shiny - Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2017). shiny: Web Application Framework for R. R package version 1.0.3. https://CRAN.R-project.org/package=shiny - ggplot2 - Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009. http://ggplot2.org - dplyr - Hadley Wickham, Romain Francois, Lionel Henry and Kirill Müller (2017). dplyr: A Grammar of Data Manipulation. R package version 0.7.0. https://CRAN.R-project.org/package=dplyr - FactoMineR - Sebastien Le, Julie Josse, Francois Husson (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software, 25(1), 1-18. 10.18637/jss.v025.i01 - dendextend - Tal Galili (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics. DOI:10.1093/bioinformatics/btv428 - networkD3 - J.J. Allaire, Christopher Gandrud, Kenton Russell and CJ Yetman (2017). networkD3: D3 JavaScript Network Graphs from R. R package version 0.4. https://CRAN.R-project.org/package=networkD3 - reshape2 - Hadley Wickham (2007). Reshaping Data with the reshape Package. Journal of Statistical Software, 21(12), 1-20. URL http://www.jstatsoft.org/v21/i12/ - rgl - Daniel Adler, Duncan Murdoch and others (2017). rgl: 3D Visualization Using OpenGL. R package version 0.98.1. https://CRAN.R-project.org/package=rgl - mzR - Chambers, C. M, et al. (2012). “A cross-platform toolkit for mass spectrometry and proteomics.” Nat Biotech, 30(10), pp. 918–920. doi: 10.1038/nbt.2377, http://dx.doi.org/10.1038/nbt.2377. Martens L, Chambers M, et al. (2010). “mzML - a Community Standard for Mass Spectrometry Data.” Mol Cell Proteomics. doi: 10.1074/mcp.R110.000133. Pedrioli PGA,et al. (2004). “A common open representation of mass spectrometry data and its application to proteomics research.” Nat Biotechnol, 22(11), pp. 1459–1466. doi: 10.1038/nbt1031. Keller A, Eng J, Zhang N, Li X and Aebersold R (2005). “A uniform proteomics MS/MS analysis platform utilizing open XML file formats.” Mol Syst Biol. Kessner D, Chambers M, Burke R, Agus D and Mallick P (2008). “ProteoWizard: open source software for rapid proteomics tools development.” Bioinformatics, 24(21), pp. 2534–2536. doi: 10.1093/bioinformatics/btn323.
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