What is MZKit? DOI

Mzkit is an open source raw data file toolkit for mass spectrometry data analysis, provides by the BioNovoGene corporation. The features of mzkit inlcudes: raw data file content viewer(XIC/TIC/Mass spectrum plot/MS-Imaging), build molecule network, formula de-novo search, de-novo annotation of the unknown metabolite features, MALDI single cell metabolomics data analysis, pathological slide viewer and targeted data quantification.

Designed for developers

The MZKit project its source code is open sourced on github and totally free of charge for used in your academic project.

Time saver

MZKit is easily to learn and also work in fast speed. You also can running the MZKit R# script in parallel for deal with the large scale metabolomics raw data files.

Focused Metabolomics

The development of the MZKit workbench software is mainly address of the metabolomics raw data processing and raw data visualization.

User-friendly

The MZKit workbench has a user friendly UI, which means you don't needs to do some scripting programming for run data analysis of your mass spectrum raw data file.

Easy to automation

You can create a mass spectrum data analysis pipeline on a windows server via scripting in R# programming language. The MZKit software is written in VisualBasic.NET language, and you also can build a software by using the MZKit library in your .NET program.

Tutorials video

Watch the MZKit tutorial video on BILIBILI which we created for the audience like you.

  MZKit Video Tutorials from “六神无主鸠”

A ready-to-use toolbox for processing your metabolomics dataset and the biological knowledges.

Simply and easily enough for handling the metabolomics rawdata, interpret your data，visualize your data and take a deep look inside into the biological knowledges!
View Documents
  • Windows App
  • Linux Docker

Discover Features

There ares some awesome features of MZKit workbench that wait for discovered by you. Download and install MZKit and then try to find out them!

Click this feature list to discover
View TIC/BPC/XIC chromatography plot KEGG biological function data analysis MS-Imaging Viewer Mass spectrum alignment Mass spectrum molecule networking LC-MS raw data viewer and data inspector Targetted metabolomics linear modelling R# automation your analysis work
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Feature Highlights


MS-Imaging data visualization of your biological samples in MZKit workbench can use for compares of your multiple targeted metabolite compounds on one graphics canvas. Such data visualization of the metabolite can visual and compares of the spatial enrichment results for the multiple targeted drugs or metabolism products for your experiment design. It is useful for presenting the biomarker in a spatially way by MS-imaging.

  Visit Data Visualization Gallery to learn more.

MS-Imaging highlights
View pathological section slide via MZKit.

MZKit workbench is compatible with the Hamamatsu slide scanner pathological section raw data file. You can open & view a pathological slide image in ndpi format or tif format in mzkit. Make pathological tissue region annotation and then mapping to the MS-imaging raw data.




Targetted Linear

Targeted metabolomics is the study and analysis of specific metabolites, and it take parts in disease research, animal model verification, biomarker discovery, disease diagnosis, drug development, drug screening, drug evaluation, clinical research and plant metabolism research:

  1. Validation
  2. Hypothesis driven
  3. Absolute quantification of spesific features Validation of identified feature (Requires commercially available chemicalstandart for validation)

 

LC-MS scatter 2D plot

Untargeted metabolomics is a "discovery mode" process and it relies on differential comparison between groups of samples (for example cases versus controls): Non-targeted metabolomics can analyze metabolites derived from the organisms comprehensively and systematically. It is an unbiased metabolomics analysis that can discover new biomarkers.

LCMS data visualization
Chemoinformatics data processing

Draw chemical molecule structure via Ketcher in MZKit, and then convert the molecule structure data as the SMILES/InChI string. Then we could do mass spectrum prediction in MZKit via calling CFM-ID tool for generates the in-silicon MS2 matrix, finally search your MS2 matrix inside multiple sample that which has been loaded into the MZKit workspace.


Runtime & System Requirements

MZKit workbench software required of .NET Framework 4.8 runtime:
https://dotnet.microsoft.com/download/dotnet-framework/net48
Rstudio application for run mzkit R# automation pipeline required of .NET Core 6.0(windows-x64) runtime:
https://dotnet.microsoft.com/en-us/download/dotnet/6.0
The Microsoft WebView2 runtime(windows-x64) is required for some interactive data visualization and analysis report generation:
https://developer.microsoft.com/en-us/microsoft-edge/webview2/
Unidata netCDF-C library is required for read GCxGC or GCMS netCDF rawdata file:
https://downloads.unidata.ucar.edu/netcdf-c/4.9.2/netCDF4.9.2-NC4-64.exe

MZKit workbench supports Windows 10/11 64Bit system and Windows Server 2016/2019/2022.

Hardware Requirement:

  • CPU: 3.0GHz CPU with 2 Core at least, latest Intel i7/i9/E3 CPU or Intel Xeon Gold 6250 is recommended.
  • Memory: 16GB DDR3 is required, 32GB DDR4 or higher is recommended.
  • Screen Resolution: 1600x900(minimal), 1920x1080 or above is recommended.
  • Graphics Card: NVIDIA GeForce GTX 1060 3GB or higher is recommended.

License & Copyright

You can cite MZKit in your literature work if needed: xie, guigang & BioNovoGene. BioNovoGene mzkit: Data toolkits for processing NMR, MALDI MSI, LC-MS and GC-MS raw data, chemoinformatics data analysis and data visualization. (2022) doi:10.5281/zenodo.7040586.


  • MZKit® is a registered trademark of BioNovoGene Corporation, protected by copyright law and international treaties.
  • RawFileReader reading tool. Copyright © 2016 by Thermo Fisher Scientific, Inc. All rights reserved.
  • MS-DIAL was launched as a universal program for untargeted metabolomics that supports multiple instruments (GC/MS, GC/MS/MS, LC/MS, and LC/MS/MS) and MS vendors (Agilent, Bruker, LECO, Sciex, Shimadzu, Thermo, and Waters). Part of the spectrum algorithm in MZKit is developed based on the MS-DIAL project.


MIT License

Copyright (c) 2018 gg.xie@bionovogene.com, BioNovoGene Co., LTD.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Reference

The development of the MZKit workbench library based on these works, you are also can cite these works if you use MZkit software:

  1. X. Shen, R. Wang, X. Xiong, Y. Yin, Y. Cai, Z. Ma, N. Liu, and Z.-J. Zhu* (Corresponding Author), Metabolic Reaction Network-based Recursive Metabolite Annotation for Untargeted Metabolomics, Nature Communications, 2019, 10: 1516.
  2. Li S, Park Y, Duraisingham S, Strobel FH, Khan N, et al. (2013) Predicting Network Activity from High Throughput Metabolomics. PLOS Computational Biology 9(7): e1003123. https://doi.org/10.1371/journal.pcbi.1003123
  3. Pang, Z., Chong, J., Zhou, G., Morais D., Chang, L., Barrette, M., Gauthier, C., Jacques, PE., Li, S., and Xia, J. (2021) MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights Nucl. Acids Res. (doi: 10.1093/nar/gkab382)
  4. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research, 27(1), 29–34. https://doi.org/10.1093/nar/27.1.29
  5. Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M. (2015). MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature methods, 12(6), 523–526. https://doi.org/10.1038/nmeth.3393
  6. Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S., LMSD: LIPID MAPS® structure database Nucleic Acids Research, 35: p. D527-32., DOI: 10.1093/nar/gkl838 , PMID: 17098933
  7. Fahy E, Sud M, Cotter D & Subramaniam S., LIPID MAPS® online tools for lipid research Nucleic Acids Research (2007), 35: p. W606-12., DOI: 10.1093/nar/gkm324 , PMID: 17584797
  8. Wishart DS, Guo AC, Oler E, et al., HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022. Jan 7;50(D1):D622–31. 34986597
  9. Mingxun Wang, Jeremy J. Carver, Vanessa V. Phelan, Laura M. Sanchez, Neha Garg, Yao Peng, Don Duy Nguyen et al. "Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking." Nature biotechnology 34, no. 8 (2016): 828. PMID: 27504778
  10. Li, Y., Kind, T., Folz, J. et al. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat Methods 18, 1524–1531 (2021). https://doi.org/10.1038/s41592-021-01331-z
  11. Kind, T., Fiehn, O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105 (2007). https://doi.org/10.1186/1471-2105-8-105

Contact

I hope you find this MZKit software useful.
Feel free to get in touch if you have any questions or suggestions.

Wanna to get supports or make contribution to MZKit?

Get in touch with me if you encounter any problem when running the MZKit workbench software; And you also can prompt or request a new data analysis feature for make improvements of the MZKit workbench software.

Xieguigang
Senior Data Scientist at PANOMIX

External Links

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