pmultiqc is a MultiQC plugin for comprehensive quality control reporting of proteomics data. It generates interactive HTML reports with visualizations and metrics to help you assess the quality of your mass spectrometry-based proteomics experiments.

• Works with multiple proteomics data formats and analysis pipelines
• Generates interactive HTML reports with visualizations
• Provides comprehensive QC metrics for MS data
• Supports different quantification methods (LFQ, TMT, DIA)
• Integrates with the MultiQC framework

pmultiqc supports the following data sources:

1. quantms pipeline output files:

   • experimental_design.tsv: Experimental design file
   • *.mzTab: Results of the identification
   • *msstats*.csv: MSstats/MSstatsTMT input files
   • *.mzML: Spectra files
   • *ms_info.tsv: MS quality control information
   • *.idXML: Identification results
   • *.yml: Pipeline parameters (optional)
   • diann_report.tsv or diann_report.parquet: DIA-NN main report (DIA analysis only)

2. MaxQuant result files:

   • parameters.txt: Analysis parameters
   • proteinGroups.txt: Protein identification results
   • summary.txt: Summary statistics
   • evidence.txt: Peptide evidence
   • msms.txt: MS/MS scan information
   • msmsScans.txt: MS/MS scan details
   • *sdrf.tsv: SDRF-Proteomics (optional)

3. DIA-NN result files:

   • report.tsv or report.parquet: DIA-NN main report
   • *sdrf.tsv: SDRF-Proteomics (optional)
   • *ms_info.parquet: mzML statistics after RAW-to-mzML conversion (using quantms-utils) (optional)

4. ProteoBench file:

   • result_performance.csv: ProteoBench result file

5. mzIdentML files:

   • *.mzid: Identification results
   • *.mzML or *.mgf: Corresponding spectra files

# To install the stable release from PyPI:
pip install pmultiqc

# Fork the repository on GitHub

# Clone the repository
git clone https://github.com/your-username/pmultiqc.git
cd pmultiqc

# Install the package locally
pip install .

# Now you can run pmultiqc on your own dataset

pmultiqc is used as a plugin for MultiQC. After installation, you can run it using the MultiQC command-line interface.

multiqc {analysis_dir} -o {output_dir}

Where:

• {analysis_dir} is the directory containing your proteomics data files
• {output_dir} is the directory where you want to save the report

# Basic usage
multiqc --quantms_plugin /path/to/quantms/results -o ./report

# With specific options
multiqc --quantms_plugin /path/to/quantms/results -o ./report --remove_decoy --condition factor

multiqc --maxquant_plugin /path/to/maxquant/results -o ./report

multiqc --diann_plugin /path/to/diann/results -o ./report

multiqc --proteobench_plugin /path/to/proteobench/files -o ./report

multiqc --mzid_plugin /path/to/mzid/files -o ./report

pmultiqc generates a comprehensive report with multiple sections:

• Experimental Design: Overview of the dataset structure
• Pipeline Performance Overview: Key metrics including:
  • Contaminants Score
  • Peptide Intensity
  • Charge Score
  • Missed Cleavages
  • ID rate over RT
  • MS2 OverSampling
  • Peptide Missing Value
• Summary Table: Spectra counts, identification rates, peptide and protein counts
• MS1 Information: Quality metrics at MS1 level
• Pipeline Results Statistics: Overall identification results
• Number of Peptides per Protein: Distribution of peptide counts per protein

• Peptide Table: First 500 peptides in the dataset
• PSM Table: First 500 PSMs (Peptide-Spectrum Matches)

• Spectra Tracking: Summary of identification results by file
• Search Engine Scores: Distribution of search engine scores
• Precursor Charges Distribution: Distribution of precursor ion charges
• Number of Peaks per MS/MS Spectrum: Peak count distribution
• Peak Intensity Distribution: MS2 peak intensity distribution
• Oversampling Distribution: Analysis of MS2 oversampling
• Delta Mass: Mass accuracy distribution
• Peptide/Protein Quantification Tables: Quantitative levels across conditions

You can find example reports on the docs page.

We have comprehensive issue templates to help you report problems effectively:

• Bug Reports: For crashes, incorrect metrics, or unexpected behavior
• Metric Requests: For new proteomics quality control metrics (we actively encourage these!)
• Feature Requests: For new visualizations, data format support, or functionality
• Service Issues: For problems with the PRIDE web service
• General Issues: For questions, suggestions, or issues that don't fit other categories

We welcome contributions! See our Contributing Guide for detailed instructions.

1. Fork the repository
2. Clone your fork: git clone https://github.com/YOUR-USERNAME/pmultiqc
3. Create a feature branch: git checkout -b new-feature
4. Make your changes
5. Install in development mode: pip install -e .
6. Test your changes: cd tests && multiqc resources/LFQ -o ./
7. Commit your changes: git commit -am 'Add new feature'
8. Push to the branch: git push origin new-feature
9. Submit a pull request

This project is licensed under the terms of the LICENSE file included in the repository.

If you use pmultiqc in your research, please cite our preprint:

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics
Qi-Xuan Yue, Chengxin Dai, Selvakumar Kamatchinathan, Chakradhar Bandla, Henry Webel, Asier Larrea, Wout Bittremieux, Julian Uszkoreit, Tom David Muller, Jinqiu Xiao, Juergen Cox, Philip Ewels, Vadim Demichev, Oliver Kohlbacher, Timo Sachsenberg, Chris Bielow, Mingze Bai, Yasset Perez-Riverol
bioRxiv 2025. DOI: 10.1101/2025.11.02.685980

Preprint: https://www.biorxiv.org/content/early/2025/11/03/2025.11.02.685980

@article{Yue2025.11.02.685980,
  author = {Yue, Qi-Xuan and Dai, Chengxin and Kamatchinathan, Selvakumar and Bandla, Chakradhar and Webel, Henry and Larrea, Asier and Bittremieux, Wout and Uszkoreit, Julian and Muller, Tom David and Xiao, Jinqiu and Cox, Juergen and Ewels, Philip and Demichev, Vadim and Kohlbacher, Oliver and Sachsenberg, Timo and Bielow, Chris and Bai, Mingze and Perez-Riverol, Yasset},
  title = {pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics},
  elocation-id = {2025.11.02.685980},
  year = {2025},
  doi = {10.1101/2025.11.02.685980},
  publisher = {Cold Spring Harbor Laboratory},
  URL = {https://www.biorxiv.org/content/early/2025/11/03/2025.11.02.685980},
  eprint = {https://www.biorxiv.org/content/early/2025/11/03/2025.11.02.685980.full.pdf},
  journal = {bioRxiv}
}