Abstract
Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two frequently used tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats, and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxy's graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high-throughput proteomics data science for everyone.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1558-1565 |
| Number of pages | 8 |
| Journal | Journal of Proteome Research |
| Volume | 21 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 3 2022 |
Bibliographical note
Funding Information:The authors thank the Galaxy community for critically reviewing tools and training materials and Tim Dudgeon for support with the MaxQuant wrapper. The authors thank Olga Vitek, Meena Choi, Ting Huang, and Mateusz Staniak (Northeastern University) and Derya Steenbuck (University of Freiburg) for providing test files and for helpful discussions. The authors acknowledge the support of the Freiburg Galaxy Team, Bioinformatics, University of Freiburg (Germany), funded by the Collaborative Research Centre 992 Medical Epigenetics (DFG Grant SFB 992/1 2012) and the German Federal Ministry of Education and Research BMBF (Grant 031 A538A de.NBI-RBC). O.S. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, SCHI 871/17-1, NY 90/6-1, SCHI 871/15-1, GR 4553/5-1, PA 2807/3-1, project-ID 431984000–SFB 1453, project-ID 441891347–SFB 1479, project-ID 423813989–GRK 2606, and project-ID 322977937–GRK 2344), the ERA PerMed programme (BMBF, 01KU1916, and 01KU1915A), the German-Israeli Foundation (Grant no. 1444), and the German Consortium for Translational Cancer Research (project Impro-Rec). B.W. acknowledges support by the DFG, Project-ID 403222702/SFB 1381, TRR 130, and FOR 2743. The tools are available at the following sites: Galaxy toolshed: https://toolshed.g2.bx.psu.edu ; European Galaxy server: https://usegalaxy.eu ; Galaxy Training Network: https://training.galaxyproject.org/training-material/topics/proteomics ; Github repository: https://github.com/galaxyproteomics/tools-galaxyp .
Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.
Keywords
- LC-MS/MS
- bioinformatics
- cloud computing
- proteomics
- reproducibility
- statistical modeling
- tandem mass spectrometry
- Reproducibility of Results
- Proteins/analysis
- Cloud Computing
- Mass Spectrometry/methods
- Proteomics/methods
- Software
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't