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StreamFind R package is developed within the project “Flexible data analysis and workflow designer to identify chemicals in the water cycle” funded by the German Federal Ministry of Education and Research (BMBF). The development is carried out by the Institut für Umwelt & Energie, Technik & Analytik e. V. (IUTA), the Forschungszentrum Informatik (FZI) and supporting partners. StreamFind is intended to be a platform for assembling processing workflows for different types of data (e.g. mass spectrometry (MS) and spectroscopy data) with applications in different fields (e.g. environmental studies of the water cycle and quality control of pharmaceuticals). StreamFind aims to stimulate the use of advanced data analysis (e.g. non-target screening, statistical analysis, etc.) in routine studies, to promote standardization of data structure and processing, and to facilitate retrospective data evaluation. The StreamFind platform is aimed at scientists, but also at technicians due to its comprehensive documentation, its well categorized set of integrated modular processing methods and its embedded graphical user interface.

The StreamFind development is ongoing, please contact us for questions or collaboration.

Installation

Pre-requisites for the StreamFind are the R software and the RTools (only applicable for Windows users). RTools is needed for compiling C++ code used in the StreamFind R package. StreamFind also uses Python scripts for some of its processing methods, so it is recommended (but not mandatory) to have the latest Python installed and added to the environmental variables for Windows users. Assuming that R, R Tools and Python (optional) are installed, the StreamFind R package can be installed from the GitHub repository via the Bioconductor Manager, which makes it easier to install necessary Bioconductor dependencies (e.g. zlibbioc and Rhdf5lib). Note that StreamFind itself is not available on Bioconductor. Please note that the default timeout for the BiocManager::install function might be to short. Thus, if the download of StreamFind fails run options(timeout = 600) before the installation command, as shown below.

options(timeout = 600) # Increase timeout
if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("odea-project/StreamFind")

Other dependencies

Some processing methods used by StreamFind depend on other open-source software. For example, when conducting non-target screening using mass spectrometry, StreamFind utilises the patRoon R package and its associated dependencies for certain processing methods. If a dependency is not installed, a warning message with instructions will be displayed. Please consult the documentation for each processing method for details of its dependencies.

Suplementary data

The supplementary StreamFindData R package holds the data used in examples and other documentation assets of the StreamFind and can also be installed from the GitHub repository.

if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("odea-project/StreamFindData")

Docker Setup

The StreamFind can also be used via the Docker container. The Docker container is a pre-configured environment with all the necessary dependencies installed. The Docker container can be built and started with the following commands.

Build the Docker container: docker build -t my-r-app .

Start the Docker container:

For Linux/macOS/bash:

docker run -it -p 3838:3838 -p 8787:8787 -v $(pwd):/app my-r-app

For Windows PowerShell:

docker run -it -p 3838:3838 -p 8787:8787 -v "${PWD}:/app" my-r-app

Once the container is up, you’ll be prompted to select the service you want to run:

  • Option 1: Starts the Shiny application, accessible at http://localhost:3838.
  • Option 2: Starts the RStudio Server, accessible at http://localhost:8787
    • Default Username is rstudio and Password is rstudio
  • Option 3: Starts both the Shiny App and RStudio Server

Documentation

The documentation and usage examples of the StreamFind R package can be found in the reference page and articles of the webpage, respectively. Note that documentation and articles are under development so not all are yet available.

References

The StreamFind is open source due to public funding and the extensive contribution from scientific literature as well as existing open source software. Below, we reference the research and software that is used within StreamFind. Please note that each open source software or research that StreamFind uses relies on other contributions. Therefore, we recommend to search within each citation for other contributions.

Bader, Tobias, Wolfgang Schulz, Klaus Kümmerer, and Rudi Winzenbacher. 2017. “LC-HRMS Data Processing Strategy for Reliable Sample Comparison Exemplified by the Assessment of Water Treatment Processes.” Analytical Chemistry 89 (24): 13219–26. https://doi.org/10.1021/acs.analchem.7b03037.
Benton, H. Paul, Elizabeth J. Want, and Timothy M. D. Ebbels. 2010. “Correction of Mass Calibration Gaps in Liquid Chromatography-Mass Spectrometry Metabolomics Data.” BIOINFORMATICS 26: 2488.
Chambers, M. C., B. Maclean, R. Burke, et al. 2012a. “A Cross-Platform Toolkit for Mass Spectrometry and Proteomics.” Nature Biotechnology 30 (10): 918–20. https://doi.org/10.1038/nbt.2377.
Chambers, Matthew C., Maclean, et al. 2012b. “A cross-platform toolkit for mass spectrometry and proteomics.” Nat Biotech 30 (10): 918–20. https://doi.org/10.1038/nbt.2377.
Djoumbou-Feunang, Yannick, Jarlei Fiamoncini, Alberto Gil-de-la-Fuente, Claudine Manach, Russell Greiner, and David S. Wishart. 2019. “BioTransformer: A Comprehensive Computational Tool for Small Molecule Metabolism Prediction and Metabolite Identification.” Journal of Cheminformatics 11 (2): 1–25. https://doi.org/10.1186/s13321-018-0324-5.
Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020. “MSnbase, Efficient and Elegant r-Based Processing and Visualisation of Raw Mass Spectrometry Data.” bioRxiv.
Gatto, Laurent, and Kathryn Lilley. 2012. “MSnbase - an r/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28: 288–89.
Helmus, Rick, Thomas L. ter Laak, Annemarie P. van Wezel, Pim de Voogt, and Emma L. Schymanski. 2021. “patRoon: Open Source Software Platform for Environmental Mass Spectrometry Based Non-Target Screening.” Journal of Cheminformatics 13 (1). https://doi.org/10.1186/s13321-020-00477-w.
Helmus, Rick, Bas van de Velde, Andrea M. Brunner, Thomas L. ter Laak, Annemarie P. van Wezel, and Emma L. Schymanski. 2022. “patRoon 2.0: Improved Non-Target Analysis Workflows Including Automated Transformation Product Screening.” Journal of Open Source Software 7 (71): 4029. https://doi.org/10.21105/joss.04029.
Ji, Hongchao, Fanjuan Zeng, Yamei Xu, Hongmei Lu, and Zhimin Zhang. 2017. “KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms.” Anal Chem. 14 (89): 7631–40. https://doi.org/10.1021/acs.analchem.7b01547.
Kapoulkine, Arseny. 2022. “Pugixml 1.13: Light-Weight, Simple and Fast XML Parser for c++ with XPath Support.” Copyright (C) 2006-2018. http://pugixml.org.
Keller, Andrew, Jimmy Eng, Ning Zhang, Xiao-jun Li, and Ruedi Aebersold. 2005. “A Uniform Proteomics MS/MS Analysis Platform Utilizing Open XML File Formats.” Mol Syst Biol.
Kessner, Darren, Matt Chambers, Robert Burke, David Agus, and Parag Mallick. 2008. “ProteoWizard: Open Source Software for Rapid Proteomics Tools Development.” Bioinformatics 24 (21): 2534–36. https://doi.org/10.1093/bioinformatics/btn323.
Kucheryavskiy, Sergey. 2020. “Mdatools – r Package for Chemometrics.” Chemometrics and Intelligent Laboratory Systems 198: 103937. https://doi.org/https://doi.org/10.1016/j.chemolab.2020.103937.
Kuhl, C., R. Tautenhahn, C. Boettcher, T. R. Larson, and S. Neumann. 2012. “CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets.” Analytical Chemistry 84: 283–89. http://pubs.acs.org/doi/abs/10.1021/ac202450g.
Martens, Lennart, Matthew Chambers, Marc Sturm, et al. 2010. “MzML - a Community Standard for Mass Spectrometry Data.” Mol Cell Proteomics, ahead of print. https://doi.org/10.1074/mcp.R110.000133.
Meringer, Markus, Stefan Reinker, Juan Zhang, and Alban Muller. 2011. “MS/MS Data Improves Automated Determination of Molecular Formulas by Mass Spectrometry.” MATCH Commun. Math. Comput. Chem 65 (2): 259–90.
Pedrioli, Patrick G A, Jimmy K Eng, Robert Hubley, et al. 2004. “A Common Open Representation of Mass Spectrometry Data and Its Application to Proteomics Research.” Nat Biotechnol 22 (11): 1459–66. https://doi.org/10.1038/nbt1031.
Reuschenbach, Max, Lotta L. Hohrenk-Danzouma, Torsten C. Schmidt, and Gerrit Renner. 2022. “Development of a Scoring Parameter to Characterize Data Quality of Centroids in High-Resolution Mass Spectra.” Analytical and Bioanalytical Chemistry 414 (July): 6635–45. https://doi.org/10.1007/s00216-022-04224-y.
Röst, Hannes L., Timo Sachsenberg, Stephan Aiche, et al. 2016. “OpenMS: A Flexible Open-Source Software Platform for Mass Spectrometry Data Analysis.” Nature Methods 13 (9): 741–48. https://doi.org/10.1038/nmeth.3959.
Ruttkies, Christoph, Steffen Neumann, and Stefan Posch. 2019. “Improving MetFrag with Statistical Learning of Fragment Annotations.” BMC Bioinformatics 20 (1): 1–14.
Ruttkies, Christoph, Emma L Schymanski, Nadine Strehmel, et al. 2019. “Supporting Non-Target Identification by Adding Hydrogen Deuterium Exchange MS/MS Capabilities to MetFrag.” Analytical and Bioanalytical Chemistry 411: 4683–700.
Ruttkies, Christoph, Emma L Schymanski, Sebastian Wolf, Juliane Hollender, and Steffen Neumann. 2016. “MetFrag Relaunched: Incorporating Strategies Beyond in Silico Fragmentation.” Journal of Cheminformatics 8 (1): 1–16.
Sheehy, Guillaume, Fabien Picot, Frédérick Dallaire, et al. 2023. “Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids.” Journal of Biomedical Optics 28 (2): 025002. https://doi.org/10.1117/1.JBO.28.2.025002.
Smith, C.A., Want, et al. 2006. “XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching and Identification.” Analytical Chemistry 78: 779–87.
Tautenhahn, Ralf, Christoph Boettcher, and Steffen Neumann. 2008. “Highly Sensitive Feature Detection for High Resolution LC/MS.” BMC Bioinformatics 9: 504.
Tisler, Selina, David I. Pattison, and Jan H. Christensen. 2021. “Correction of Matrix Effects for Reliable Non-Target Screening LC–ESI–MS Analysis of Wastewater.” Analytical Chemistry 93 (24): 8432–41. https://doi.org/10.1021/acs.analchem.1c00357.
Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with s. Fourth. Springer. https://www.stats.ox.ac.uk/pub/MASS4/.
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Wishart, David S., Siyang Tian, Dana Allen, et al. 2022. “BioTransformer 3.0 – a Web Server for Accurately Predicting Metabolic Transformation Products.” Nucleic Acids Research.
Wolf, Sebastian, Stephan Schmidt, Matthias Müller-Hannemann, and Steffen Neumann. 2010. “In Silico Fragmentation for Computer Assisted Identification of Metabolite Mass Spectra.” BMC Bioinformatics 11: 1–12.
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