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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.

Qi-Xuan Yue1, Chengxin Dai2, Selvakumar Kamatchinathan3

  • 1Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.

Molecular & Cellular Proteomics : MCP
|February 19, 2026
PubMed
Summary

pmulti-qc is a new Python package for quality control in proteomics. It generates standardized, web-based reports across multiple analysis platforms, improving data reliability and reproducibility.

Keywords:
DIAFAIRSingle celllarge-scale data analysisquality controlreproducibility

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Data Science

Background:

  • The growing scale and complexity of proteomics data necessitate advanced quality control (QC) frameworks.
  • Ensuring data reliability and reproducibility is crucial for advancing proteomics research.

Purpose of the Study:

  • To introduce pmultiqc, an open-source Python package for standardizing and generating web-based QC reports for proteomics data.
  • To provide a scalable and interpretable QC framework across diverse proteomics data analysis platforms.

Main Methods:

  • Developed pmultiqc as an extension of the MultiQC framework with specialized modules for mass spectrometry workflows.
  • Integrated support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines.
  • Implemented metadata-aware QC using sample metadata in the SDRF format for standardized reporting.

Main Results:

  • pmultiqc computes key QC metrics including intensity distributions, identification rates, retention time consistency, and missing value patterns.
  • Generates interactive, publication-ready reports with metadata-guided QC metrics.
  • Offers a modular architecture for easy extension to new workflows and formats.

Conclusions:

  • pmultiqc provides a robust, scalable, and interpretable QC solution for the proteomics community.
  • The package enhances data reliability and reproducibility through standardized, metadata-aware reporting.
  • Accessible via local installation or a cloud-based service for broad user adoption.