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streamFind
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所属分类：大数据
开发工具：R
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上传日期：2023-08-20 20:54:04
上 传 者：sh-1993
说明：  用于非目标筛选和高级数据分析的工作流设计器，
(Workflow Designer for Non-target Screening and Advanced Data Analysis,)
文件列表:
.Rbuildignore (264, 2023-11-02)
.lintr (192, 2023-11-02)
DESCRIPTION (1514, 2023-11-02)
LICENSE.md (34904, 2023-11-02)
NAMESPACE (5294, 2023-11-02)
R/ (0, 2023-11-02)
R/RcppExports.R (2304, 2023-11-02)
R/class_R6_MassSpecData.R (263901, 2023-11-02)
R/class_R6_RamanData.R (8496, 2023-11-02)
R/class_S3_MassSpecAnalysis.R (17882, 2023-11-02)
R/class_S3_ProcessingSettings.R (8311, 2023-11-02)
R/class_S3_ProjectHeaders.R (3453, 2023-11-02)
R/fct_ms_convert_trim_files_.R (11687, 2023-11-02)
R/fct_ms_default_ProcessingSettings.R (64016, 2023-11-02)
R/fct_ms_make_ms_targets.R (13235, 2023-11-02)
R/generics.R (1439, 2023-11-02)
R/global_import_namespace.R (1483, 2023-11-02)
R/methods_S3_ms_annotate_features.R (3615, 2023-11-02)
R/methods_S3_ms_bin_spectra.R (591, 2023-11-02)
R/methods_S3_ms_calculate_quality.R (15230, 2023-11-02)
R/methods_S3_ms_centroid_spectra.R (1824, 2023-11-02)
R/methods_S3_ms_control_standards.R (2, 2023-11-02)
R/methods_S3_ms_correct_intensity.R (2, 2023-11-02)
R/methods_S3_ms_find_features.R (8938, 2023-11-02)
R/methods_S3_ms_find_internal_standards.R (4860, 2023-11-02)
R/methods_S3_ms_group_features.R (6245, 2023-11-02)
R/methods_S3_ms_suspect_screening.R (4238, 2023-11-02)
R/streamFind-package.R (138, 2023-11-02)
R/utils_ms.R (5839, 2023-11-02)
R/utils_ms_plots.R (57402, 2023-11-02)
R/utils_pipe.R (363, 2023-11-02)
_pkgdown.yml (3373, 2023-11-02)
dev/ (0, 2023-11-02)
dev/demo_ozonation.R (4017, 2023-11-02)
dev/dev_ProcessingSettings.R (4047, 2023-11-02)
dev/dev_annotate_features.R (13176, 2023-11-02)
dev/dev_assets.R (2368, 2023-11-02)
... ...
# StreamFind (R package) [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

Logo

The StreamFind project, entitled “Flexible data analysis and workflow designer to identify chemicals in the water cycle”, is funded by the [Bundesministerium für Bildung und Forschung (BMBF)](https://www.bmbf.de) and is a cooperation between the [Institut für Umwelt & Energie, Technik & Analytik e. V. (IUTA)](https://www.iuta.de), the [Forschungszentrum Informatik (FZI)](https://www.fzi.de/) and supporting partners. The goal of the StreamFind project is the development and assembly of data processing workflows for mass spectrometry and spectroscopy and the application of the workflows in environmental and quality studies of the water cycle. The StreamFind aims to stimulate the use of advanced data analysis (e.g., non-target screening, statistical analysis, etc.) in routine studies, promoting standardization of data processing and structure and easing the retrospective evaluation of data. The StreamFind platform is directed to academics but also technicians, due to the aspired comprehensive documentation, well categorized set of integrated modular functions and the graphical user interface.
The library of [StreamFind](https://github.com/odea-project/StreamFind) is an R package (this repository). The graphical user interface (GUI) is a Web App developed in JavaScript and can be found in the [StreamFind-App](https://github.com/odea-project/StreamFind-App) repository. The StreamFind development is ongoing, please [contact us](mailto:cunha@iuta.de) for questions or collaboration. ## Installation For installation of the StreamFind R package, it is recommended to first install the dependencies. Besides [R](https://cran.r-project.org/) and [RTools](https://cran.r-project.org/bin/windows/Rtools/) (the latter is only recommended for Windows users), the StreamFind depends on the [patRoon](https://github.com/rickhelmus/patRoon) R package and its dependencies. The patRoon R package combines several tools for basic and advanced data processing and can be used interchangeably with the StreamFind R package. Installation instructions for patRoon and its dependencies can be found [here](https://rickhelmus.github.io/patRoon/handbook_bd/manual-installation.html#r-prerequisites). Then, the StreamFind R package can be installed from the GitHub repository. ``` r remotes::install_github("odea-project/StreamFind", dependencies = TRUE) ``` The supplementary [StreamFindData](https://github.com/odea-project/StreamFindData) R package holds the data used in examples and other documentation assets of the StreamFind R package and can also be installed from the GitHub repository. ``` r remotes::install_github("odea-project/StreamFindData") ``` ### Documentation The documentation and usage examples of the StreamFind R package can be found in the [reference page](https://odea-project.github.io/StreamFind/reference/index.html) and [articles](https://odea-project.github.io/StreamFind/articles/index.html) of the [webpage](https://odea-project.github.io/StreamFind/index.html). # 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.
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, D Amodei, D. L. Ruderman, S. Neumann, L. Gatto, et al. 2012a. “A Cross-Platform Toolkit for Mass Spectrometry and Proteomics.” *Nature Biotechnology* 30 (10): 918–20. .
Chambers, Matthew C., Maclean, Brendan, Burke, Robert, Amodei, et al. 2012b. “A cross-platform toolkit for mass spectrometry and proteomics.” *Nat Biotech* 30 (10): 918–20. .
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). .
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. .
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. .
Kapoulkine, Arseny. 2022. “Pugixml 1.13: Light-Weight, Simple and Fast XML Parser for c++ with XPath Support.” *Copyright (C) 2006-2018*. .
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. .
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. .
Martens, Lennart, Matthew Chambers, Marc Sturm, Darren Kessner, Fredrik Levander, Jim Shofstahl, Wilfred H Tang, et al. 2010. “MzML - a Community Standard for Mass Spectrometry Data.” *Mol Cell Proteomics*. .
Pedrioli, Patrick G A, Jimmy K Eng, Robert Hubley, Mathijs Vogelzang, Eric W Deutsch, Brian Raught, Brian Pratt, et al. 2004. “A Common Open Representation of Mass Spectrometry Data and Its Application to Proteomics Research.” *Nat Biotechnol* 22 (11): 1459–66. .
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. .
Rst, Hannes L., Timo Sachsenberg, Stephan Aiche, Chris Bielow, Hendrik Weisser, Fabian Aicheler, Sandro Andreotti, et al. 2016. “OpenMS: A Flexible Open-Source Software Platform for Mass Spectrometry Data Analysis.” *Nature Methods* 13 (9): 741–48. .
Smith, C.A., Want, E.J., O’Maille, G., Abagyan,R., Siuzdak, and G. 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.
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