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

Maximilian T. Strauss (), Isabell Bludau, Wen-Feng Zeng, Eugenia Voytik, Constantin Ammar, Julia P. Schessner, Rajesh Ilango, Michelle Gill, Florian Meier, Sander Willems and Matthias Mann ()
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Maximilian T. Strauss: Max Planck Institute of Biochemistry
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
Sander Willems: Max Planck Institute of Biochemistry
Matthias Mann: Max Planck Institute of Biochemistry

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46485-4

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