MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone

Pinter, Niko; Glätzer, Damian; Fahrner, Matthias; Fröhlich, Klemens; Johnson, James E.; Grüning, Björn; Warscheid, Bettina; Drepper, Friedel; Schilling, Oliver; Föll, Melanie Christine

doi:10.1021/acs.jproteome.2c00051

Cited by 12 publications

(9 citation statements)

References 33 publications

(48 reference statements)

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“…Coupled with the flexible, modular nature of Galaxy, users are able to easily select, test, and replace tools as new tools are made available to best suit their analyses. The Galaxy platform is accessible via high-performance computing (HPC) systems and public cloud environments (22).…”

Section: Discussionmentioning

confidence: 99%

“…The human and microbial protein groups were separately analyzed using the MSstatsTMT tool. The MSstatsTMT tool is a free, open-source R/Bioconductor package that is compatible with data processing, such as MaxQuant, and allows for sensitive and specific detection of differentially expressed proteins in large-scale experiments with multiple biological samples (22,24).…”

Section: Data Interpretation Module Using Unipept and Msstatstmtmentioning

confidence: 99%

“…Proteins and peptides from the MS files were quantified using MaxQuant against the verified microbial protein sequences database (merged with human protein sequences and contaminants sequences; 82,562 protein sequences). This module generated lists of quantified proteins and peptides that were processed for data interpretation and visualization (22). To perform data analysis of the microbial community present in the sample, microbial peptides were extracted from the list of identified peptides.…”

Section: Quantification Module Using Maxquantmentioning

confidence: 99%

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A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Do,

Mehta,

Wagner

et al. 2023

Preprint

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Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

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Section: Discussionmentioning

confidence: 99%

Section: Data Interpretation Module Using Unipept and Msstatstmtmentioning

confidence: 99%

Section: Quantification Module Using Maxquantmentioning

confidence: 99%

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A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Do,

Mehta,

Wagner

et al. 2023

Preprint

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“…MaxQuant evidence and protein group files were imported into the European Galaxy server ( https://usegalaxy.eu ) [ 19 ] and analyzed with MSstats (v 3.22.0.1) [ 20 , 21 ] and basic filter tools were applied separately for each tissue. To investigate the degree of time-dependent proteome alterations, the first time point (6 h post-mortem), was used as a reference time point.…”

Section: Methodsmentioning

confidence: 99%

Proteome alterations in human autopsy tissues in relation to time after death

Kocsmár

Schmid

Cosenza-Contreras

et al. 2023

Cell. Mol. Life Sci.

Self Cite

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Protein expression is a primary area of interest for routine histological diagnostics and tissue-based research projects, but the limitations of its post-mortem applicability remain largely unclear. On the other hand, tissue specimens obtained during autopsies can provide unique insight into advanced disease states, especially in cancer research. Therefore, we aimed to identify the maximum post-mortem interval (PMI) which is still suitable for characterizing protein expression patterns, to explore organ-specific differences in protein degradation, and to investigate whether certain proteins follow specific degradation kinetics. Therefore, the proteome of human tissue samples obtained during routine autopsies of deceased patients with accurate PMI (6, 12, 18, 24, 48, 72, 96 h) and without specific diseases that significantly affect tissue preservation, from lungs, kidneys and livers, was analyzed by liquid chromatography–tandem mass spectrometry (LC–MS/MS). For the kidney and liver, significant protein degradation became apparent at 48 h. For the lung, the proteome composition was rather static for up to 48 h and substantial protein degradation was detected only at 72 h suggesting that degradation kinetics appear to be organ specific. More detailed analyses suggested that proteins with similar post-mortem kinetics are not primarily shared in their biological functions. The overrepresentation of protein families with analogous structural motifs in the kidney indicates that structural features may be a common factor in determining similar postmortem stability. Our study demonstrates that a longer post-mortem period may have a significant impact on proteome composition, but sampling within 24 h may be appropriate, as degradation is within acceptable limits even in organs with faster autolysis.

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“…Several applications have been developed to increase the accessibility of postsearch quantitative proteomic analysis methods to the wider proteomic community. The Galaxy project has recently implemented MSstats and MSstatsTMT directly into their GUI, . , Users have access to the core functionalities of the packages, including converters for several spectral processing tools, missing value imputation, feature summarization, and differential analysis. While the core functionalities are available, many spectral processing converters are currently missing, including tools for Progenesis (Nonlinear Dynamics/Waters), Proteome Discoverer, Spectronaut, DIA-Umpire, SpectroMine (Biognosys), and Philosopher, meaning that users of these tools need to manually convert the data into the correct format before using Galaxy.…”

Section: Introductionmentioning

confidence: 99%

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

et al. 2023

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Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/ MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

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MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone

Cited by 12 publications

References 33 publications

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Proteome alterations in human autopsy tissues in relation to time after death

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

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