FragPipe is a comprehensive computational platform for analyzing mass spectrometry-based proteomics data. FragPipe comes with an easy to use Java Graphical User Interface (GUI) but can also be run in the command line mode, on Windows, Linux, or in the cloud environment. It is powered by MSFragger - an ultrafast proteomic search engine suitable for both conventional and "open" (wide precursor mass tolerance) peptide identification. FragPipe includes Percolator and 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. FragPipe includes MSBooster module for deep-learning based rescoring of peptide identifications. 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, DIA-Umpire SE, and diaTracer modules for direct ("library-free") analysis of data independent acquisition (DIA) data. FragPipe includes DIA-NN for extraction of quantification from DIA data.

Download

Docker image

FragPipe tutorials

• Using FragPipe (general tutorial covering all FragPipe modules)
• Running FragPipe in command line interface
• Pulling and running FragPipe using Docker
• PTM discovery
  • Open search
  • Mass offset search
  • Labile PTM search
  • Glycoproteomics search
  • Custom mass offset workflow (RNA crosslinking example)
  • Diagnostic ion mining
  • FPOP
• TMT/iTRAQ quantification
  • Single plex
  • Multiple plexes with a pooled reference sample
  • Streamlined activity-based protein profiling of reactive cysteines (SLC-ABPP)
• Label-free quantification
• SILAC (or other MS1-labeled) data
• DIA analysis
• Novel/variant peptide detection using two-pass search
• Group FDR estimation for novel/variant peptide analysis
• Use peptide prediction models from Koina for MSBooster feature generation

Resources

• Interpreting output files
• List of built-in workflows
• FragPipe setup
• Converting LC/MS data files to mzML
• Setting up FragPipe on remote Linux server (with X forwarding)

Using FragPipe with other tools

• Running MSstats with IonQuant results
• Importing results into Skyline
• Importing results into Perseus

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
DIA-Umpire pseudo-MS/MS generation	✔	✔
diaTracer pseudo-MS/MS generation			✔
MSFragger search	✔	✔	✔	✔
MSFragger-DIA	✔	✔
Crystal-C artifact removal	✔	✔
PTMProphet localization	✔	✔	✔
PTM-Shepherd summarization	✔	✔	✔
Label-free quantification	✔	✔	✔
SILAC/dimethyl quantification	✔	✔	✔
TMT/iTRAQ quantification	✔	✔
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 nesvilab.org

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):
  • Read Pulling and running FragPipe using Docker

Integration

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

• Skyline
• AlphaPeptDeep
• AlphaPeptStats
• AlphaMap
• directLFQ
• DIA-NN
• MSstats
• picked_group_fdr
• FragPipe-Analyst

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.

PTM

• 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.

DIA

• 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.

Miscellaneous

• 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/Bundle.properties
     Property: msfragger.gui.version
   • File: MSFragger-GUI/build.gradle
     Property: version
   • File: MSFragger-GUI/src/umich/msfragger/gui/Bundle.properties
     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.