Ferrero-Bordera Borja, Becher Dörte, Maaß Sandra
Department of Microbial Proteomics, Institute of Microbiology, Center of Functional Genomics of Microbes, University of Greifswald, Greifswald, Germany.
Institute of Medical Psychology, Medical Faculty, Ludwig-Maximilians-University Munich, Munich, Germany.
Proteomics. 2025 May;25(9-10):e202400417. doi: 10.1002/pmic.202400417. Epub 2025 Apr 26.
The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically, absolute protein abundances. However, the complex nature of proteomics data analysis necessitates specialised expertise, making the integration of this data into models challenging. Therefore, the development of software tools that ease the analysis of proteomics data and bridge between disciplines is crucial for advancing the field of systems biology. We developed an open access Python-based software tool available either as downloadable library or as web-based graphical user interface (GUI). The pipeline simplifies the extraction and calculation of protein abundances from unprocessed proteomics data, accommodating a range of experimental approaches based on label-free quantification. Our tool was conceived as a versatile and robust pipeline designed to ease and simplify data analysis, thereby improving reproducibility between researchers and institutions. Moreover, the robust modular structure of Alpaca allows its integration with other software tools.
系统生物学中计算模型的精确构建严重依赖于定量蛋白质组学数据的可用性，特别是绝对蛋白质丰度。然而，蛋白质组学数据分析的复杂性需要专门的专业知识，这使得将这些数据整合到模型中具有挑战性。因此，开发能够简化蛋白质组学数据分析并跨学科搭建桥梁的软件工具对于推动系统生物学领域的发展至关重要。我们开发了一个基于Python的开放获取软件工具，它既可以作为可下载的库，也可以作为基于网络的图形用户界面（GUI）。该流程简化了从未处理的蛋白质组学数据中提取和计算蛋白质丰度的过程，适用于一系列基于无标记定量的实验方法。我们的工具被设计为一个通用且强大的流程，旨在简化和简化数据分析，从而提高研究人员和机构之间的可重复性。此外，Alpaca强大的模块化结构使其能够与其他软件工具集成。
