期刊
JOURNAL OF PROTEOME RESEARCH
卷 21, 期 6, 页码 1558-1565出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.2c00051
关键词
LC-MS/MS; tandem mass spectrometry; proteomics; bioinformatics; statistical modeling; cloud computing; reproducibility
资金
- Freiburg Galaxy Team, Bioinformatics, University of Freiburg (Germany) - Collaborative Research Centre 992 Medical Epigenetics (DFG) [SFB 992/1 2012]
- German Federal Ministry of Education and Research BMBF [031 A538A de.NBI-RBC]
- Deutsche Forschungsgemeinschaft (DFG) [SCHI 871/17-1, NY 90/6-1, SCHI 871/15-1, GR 4553/5-1, PA 2807/3-1, 431984000-SFB 1453, 441891347-SFB 1479, 423813989-GRK 2606, 322977937-GRK 2344]
- ERA PerMed programme (BMBF) [01KU1916, 01KU1915A]
- German-Israeli Foundation [1444]
- German Consortium for Translational Cancer Research
- DFG [403222702/SFB 1381, TRR 130, FOR 2743]
Quantitative mass spectrometry-based proteomics is a high-throughput technology for identifying and quantifying proteins in complex samples. Researchers have integrated MaxQuant and MSstats into the open-source Galaxy framework, enabling accessible and reproducible proteomics analysis in a cloud environment. This integration can be combined with thousands of existing Galaxy tools, providing the foundation for high-throughput proteomics data science for everyone.
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.
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