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AlphaPept: a modern and open framework for MS-based proteomics

Author

Listed:
  • Maximilian T. Strauss

    (Max Planck Institute of Biochemistry
    University of Copenhagen)

  • Isabell Bludau

    (Max Planck Institute of Biochemistry)

  • Wen-Feng Zeng

    (Max Planck Institute of Biochemistry)

  • Eugenia Voytik

    (Max Planck Institute of Biochemistry)

  • Constantin Ammar

    (Max Planck Institute of Biochemistry)

  • Julia P. Schessner

    (Max Planck Institute of Biochemistry)

  • Rajesh Ilango

    (Nvidia Corporation)

  • Michelle Gill

    (Nvidia Corporation)

  • Florian Meier

    (Max Planck Institute of Biochemistry
    Jena University Hospital)

  • Sander Willems

    (Max Planck Institute of Biochemistry)

  • Matthias Mann

    (Max Planck Institute of Biochemistry
    University of Copenhagen)

Abstract

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Suggested Citation

  • Maximilian T. Strauss & Isabell Bludau & Wen-Feng Zeng & Eugenia Voytik & Constantin Ammar & Julia P. Schessner & Rajesh Ilango & Michelle Gill & Florian Meier & Sander Willems & Matthias Mann, 2024. "AlphaPept: a modern and open framework for MS-based proteomics," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46485-4
    DOI: 10.1038/s41467-024-46485-4
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    References listed on IDEAS

    as
    1. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Yangyang Bian & Runsheng Zheng & Florian P. Bayer & Cassandra Wong & Yun-Chien Chang & Chen Meng & Daniel P. Zolg & Maria Reinecke & Jana Zecha & Svenja Wiechmann & Stephanie Heinzlmeir & Johannes Sch, 2020. "Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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