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A cross-platform Graphical User Interface (GUI) for running MSFragger and Philosopher - powered pipeline for comprehensive analysis of shotgun proteomics data



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FragPipe is a Java Graphical User Interface (GUI) for a suite of computational tools enabling comprehensive analysis of mass spectrometry-based proteomics data. It is powered by MSFragger - an ultrafast proteomic search engine suitable for both conventional and "open" (wide precursor mass tolerance) peptide identification. FragPipe includes the Philosopher toolkit for downstream post-processing of MSFragger search results (PeptideProphet, iProphet, ProteinProphet), FDR filtering, label-based quantification, and multi-experiment summary report generation. Crystal-C and PTM-Shepherd are included to aid interpretation of open search results. Also included in FragPipe binary are TMT-Integrator for TMT/iTRAQ isobaric labeling-based quantification, IonQuant for label-free quantification with FDR-controlled match-between-run (MBR) functionality, spectral library building with EasyPQP, and MSFragger-DIA and DIA-Umpire SE modules for direct analysis of data independent acquisition (DIA) data.

FragPipe tutorials


Using FragPipe with other tools

Supported instruments and file formats

The table below shows the compatibility of FragPipe workflow components with different spectral file formats.

Bruker .d indicates ddaPASEF files from timsTOF, other Bruker .d files should be converted to .mzML. Please also note that timsTOF data requires Visual C++ Redistributable for Visual Studio 2017 in Windows. If you see an error saying cannot find Bruker native library, please try to install the Visual C++ redistibutable.

Workflow Step .mzML Thermo (.raw) Bruker (.d) .mgf
MSFragger search
Label-free quantification
SILAC/dimethyl quantification
TMT/iTRAQ quantification
Crystal-C artifact removal
PTMProphet localization
PTM-Shepherd summarization
DIA-Umpire signal extraction
Spectral library generation
DIA-NN quantification ✔*

DIA data acquired with overlapping/staggered windows must be converted to mzML with demultiplexing. Quantification from Thermo .raw files with DIA-NN requires installation of Thermo MS File Reader, see the DIA-NN documentation for details.

Please note TMT/iTRAQ quantification from Thermo .raw files will take longer than from .mzML files.

Additional Documentation

Complete MSFragger documentation can be found on the MSFragger wiki. For documentation on the Philosopher toolkit see the Philosopher wiki.

Questions and Technical Support

View previous questions/bug reports in the FragPipe issue tracker. Please post any new questions/bug reports regarding FragPipe itself here as well. For questions specific to individual components of FragPipe you can also use MSFragger issue tracker, Philosopher issue tracker, IonQuant issue tracker. See the MSFragger wiki and FAQ.

For other tools developed by Nesvizhskii lab, visit our website

How to Run

  • Windows:
    • Double click the FragPipe.exe or FragPipe.bat from the bin folder
    • Or execute the command: java -jar FragPipe-x.x.jar
  • Linux:
    • Run the fragpipe shell script (can double-click to run)
    • Or execute the command: java -jar FragPipe-x.x.jar
  • Mac OS (command line interface only):
    • Install docker by following the instruction
    • Open terminal and pull the docker image by running docker pull fcyucn/fragpipe
    • FragPipe is located in /fragpipe_bin
    • Go to /fragpipe_bin/fragPipe-x.x/fragpipe/bin directory and execute ./fragpipe --help in the terminal


FragPipe is open source and the output is currently supported by the following software projects:

Key references

Database search
  • Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D., & Nesvizhskii, A. I. (2017). MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nature Methods, 14(5), 513-520.
  • Yu, F., Teo, G. C., Kong, A. T., Haynes, S. E., Avtonomov, D. M., Geiszler, D. J., & Nesvizhskii, A. I. (2020). Identification of modified peptides using localization-aware open search. Nature Communications, 11(1), 1-9.
  • Yu, F., Haynes, S. E., Teo, G. C., Avtonomov, D. M., Polasky, D. A., & Nesvizhskii, A. I. (2020). Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Molecular & Cellular Proteomics, 10(9), 1575-1585.
  • Teo, G. C., Polasky, D. A., Yu, F., Nesvizhskii, A. I. (2020). A fast deisotoping algorithm and its implementation in the MSFragger search engine. Journal of Proteome Research, 20(1), 498-505.
Glyco/Labile search
  • Polasky, D. A., Yu, F., Teo, G. C., & Nesvizhskii, A. I. (2020). Fast and Comprehensive N-and O-glycoproteomics analysis with MSFragger-Glyco. Nature Methods, 17, 1125-1132.
  • Polasky, D. A., Geiszler, D. J., Yu, F., & Nesvizhskii, A. I. (2022). Multiattribute Glycan Identification and FDR Control for Glycoproteomics. Molecular & Cellular Proteomics, 21(3), 100205.
  • Polasky, D. A., Geiszler, D. J., Yu, F., Kai, Li., Teo, G. C., & Nesvizhskii, A. I. (2023). MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics. Molecular & Cellular Proteomics, 22(5), 100538.
  • Chang, H. Y., Kong, A. T., da Veiga Leprevost, F., Avtonomov, D. M., Haynes, S. E., & Nesvizhskii, A. I. (2020). Crystal-C: A computational tool for refinement of open search results. Journal of Proteome Research, 19(6), 2511-2515.
  • Geiszler, D. J., Kong, A. T., Avtonomov, D. M., Yu, F., da Veiga Leprevost, F., & Nesvizhskii, A. I. (2020). PTM-Shepherd: analysis and summarization of post-translational and chemical modifications from open search results. Molecular & Cellular Proteomics, 20, 100018.
  • Geiszler, D. J., Polasky, D. A., Yu, F., & Nesvizhskii, A. I. (2023). Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides. Nature Communications, 14, 4132.
  • Tsou, C. C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A. C., & Nesvizhskii, A. I. (2015). DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nature methods, 12(3), 258-264.
  • Yu, F, Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X. , Demichev, V, Nesvizhskii, A..I. (2023). Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform, Nature Communications 14:4154.
DDA quantification
  • Yu, F., Haynes, S. E., & Nesvizhskii, A. I. (2021). IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Molecular & Cellular Proteomics, 20, 100077.
  • da Veiga Leprevost, F., Haynes, S. E., Avtonomov, D. M., Chang, H. Y., Shanmugam, A. K., Mellacheruvu, D., Kong, A. T., & Nesvizhskii, A. I. (2020). Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nature Methods, 17(9), 869-870.
  • Yang, K. L., Yu, F., Teo, G. C., Kai, L., Demichev, V., Ralser, M., & Nesvizhskii, A. I. (2023). MSBooster: improving peptide identification rates using deep learning-based features. Nature Communications, 14, 4539.

Building from scratch

  1. Update build version:
    The version of the build is stored in 3 separate places:

    • File: MSFragger-GUI/src/umich/msfragger/gui/
      Property: msfragger.gui.version
    • File: MSFragger-GUI/build.gradle
      Property: version
    • File: MSFragger-GUI/src/umich/msfragger/gui/
      Property: msfragger.gui.version
  2. Build:
    You don't need to have Gradle installed, the Gradle wrapper included in this repository will be used. From the root directory of the repository issue the following commands:

    cd ./MSFragger-GUI
    ./gradlew makeReleaseZipNoJre

    or use this version to build with Java Runtime (for Windows only):

    cd ./MSFragger-GUI
    ./gradlew makeReleaseZipWithJre
  3. The .zip output will be in MSFragger-GUI/build/github-release.


A cross-platform Graphical User Interface (GUI) for running MSFragger and Philosopher - powered pipeline for comprehensive analysis of shotgun proteomics data








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