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Background <p>Analysing large-scale mass spectrometry-based complex proteomics datasets often overwhelm desktop computational resources and require manual configuration for analysis. While <i>FragPipe</i> delivers rapid peptide identification across diverse sample preparation and acquisition modes (DDA, DIA, TMT), it remains challenging to deploy at scale.</p> Results <p>We introduce <i>Frag’n’Flow</i>, a <i>Nextflow</i>‐based pipeline that encapsulates <i>FragPipe</i>, automates input manifest and workflow generation, manages tool dependencies and includes downstream data analysis options to enable reproducible, high‐performance analyses on HPC, cloud, and cluster environments. Benchmarking against other workflow-based solutions shows that our pipeline maintains quantitative accuracy and cuts runtime nearly in half on a typical DIA dataset of ~ 58&#xa0;GB, while alleviating memory and I/O bottlenecks. We validate <i>Frag’n’Flow</i> results across three representative datasets, label-free DDA, DIA, and TMT, successfully recapitulating published biological signatures with minimal user intervention.</p> Conclusions <p>By combining the sensitivity and speed of <i>FragPipe</i> with <i>Nextflow</i>’s orchestration, <i>Frag’n’Flow</i> enables the analysis of large‐scale proteomics data, empowering the scientific community, without extensive computation expertise, to extract new insights from existing MS datasets. <i>Frag’n’Flow</i> is available at: <a href="https://github.com/ronalabrcns/FragNFlow">https://github.com/ronalabrcns/FragNFlow</a>.</p> Graphical abstract <p></p>

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Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月4日

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  • Istvan Szepesi-Nagy,
  • Roberta Borosta,
  • Zoltan Szabo,
  • Gabor E. Tusnady,
  • Lorinc S. Pongor,
  • Gergely Rona

摘要

Background

Analysing large-scale mass spectrometry-based complex proteomics datasets often overwhelm desktop computational resources and require manual configuration for analysis. While FragPipe delivers rapid peptide identification across diverse sample preparation and acquisition modes (DDA, DIA, TMT), it remains challenging to deploy at scale.

Results

We introduce Frag’n’Flow, a Nextflow‐based pipeline that encapsulates FragPipe, automates input manifest and workflow generation, manages tool dependencies and includes downstream data analysis options to enable reproducible, high‐performance analyses on HPC, cloud, and cluster environments. Benchmarking against other workflow-based solutions shows that our pipeline maintains quantitative accuracy and cuts runtime nearly in half on a typical DIA dataset of ~ 58 GB, while alleviating memory and I/O bottlenecks. We validate Frag’n’Flow results across three representative datasets, label-free DDA, DIA, and TMT, successfully recapitulating published biological signatures with minimal user intervention.

Conclusions

By combining the sensitivity and speed of FragPipe with Nextflow’s orchestration, Frag’n’Flow enables the analysis of large‐scale proteomics data, empowering the scientific community, without extensive computation expertise, to extract new insights from existing MS datasets. Frag’n’Flow is available at: https://github.com/ronalabrcns/FragNFlow.

Graphical abstract