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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools.

Shijie He1, Fangfei Zhang2

  • 1School of Life Sciences, Westlake University.

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|September 8, 2025
PubMed
Summary
This summary is machine-generated.

This study provides a beginner's guide to analyzing mass spectrometry proteomics data, covering both Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) methods. It demonstrates workflows using free tools like FragPipe and DIA-NN for effective data interpretation.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry (MS)-based proteomics, including Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA), is crucial in biological research.
  • Limited demonstrations exist on effectively searching and analyzing these complex proteomic datasets for validation studies.

Purpose of the Study:

  • To illustrate a comprehensive workflow for searching and analyzing DDA and DIA proteomic datasets.
  • To provide essential protocols for post-processing protein search results, including generating volcano plots and identifying dysregulated proteins.
  • To serve as a beginner's guide for researchers handling proteomic data.

Main Methods:

  • Utilized one DDA and one DIA dataset from the PRoteomics IDEntifications Database (PRIDE).
  • Employed FragPipe (v22.0) for DDA data analysis and DIA-NN (2.1.0) for DIA data analysis.
  • Applied R code for downstream post-processing, including statistical analysis and visualization.

Main Results:

  • Demonstrated a clear, step-by-step protocol for proteomic data analysis.
  • Successfully generated volcano plots and lists of dysregulated proteins from both DDA and DIA datasets.
  • Validated the utility of free computational tools for robust proteomic data interpretation.

Conclusions:

  • This work provides foundational protocols for searching and analyzing proteomic data, empowering researchers.
  • The demonstrated workflows enable effective handling and interpretation of MS-based proteomics datasets.
  • Researchers can leverage these methods to enhance biological investigations using proteomic data.