2025年10月21日，马克斯·普朗克生物化学研究所Matthias Mann团队在《自然—生物技术》杂志发表论文，宣布他们的研究开发出了AlphaDIA为无特征蛋白质组学提供DIA迁移学习。

在这里，该团队提出了AlphaDIA，一个模块化的开放式搜索框架，用于数据独立获取（DIA）蛋白质组学。研究人员开发了一种无特征识别算法，可以直接对原始信号进行机器学习，特别适合于检测由飞行时间仪器产生的数据中的模式。基准测试展示了竞争性识别和量化绩效。虽然该方法支持经验谱库，但该团队提出了一种名为DIA迁移学习的搜索策略，该策略可以充分预测主题库。这需要不断优化深度神经网络，以预测机器特定的和实验特定的特性，使任何翻译后修饰的通用DIA分析成为可能。AlphaDIA提供了一个高性能和可访问的框架，运行在本地或云中，向社区开放DIA分析。

据悉，基于质谱的蛋白质组学和现代采集策略产生的数据规模对生物信息学分析提出了挑战。搜索引擎需要在保持统计严谨性、透明性和性能的同时，对生物发现的数据进行优化。

附：英文原文

Title: AlphaDIA enables DIA transfer learning for feature-free proteomics

Author: Wallmann, Georg, Skowronek, Patricia, Brennsteiner, Vincenth, Lebedev, Mikhail, Thielert, Marvin, Steigerwald, Sophia, Kotb, Mohamed, Despard, Oscar, Heymann, Tim, Zhou, Xie-Xuan, Strauss, Maximilian T., Ammar, Constantin, Willems, Sander, Schwrer, Magnus, Zeng, Wen-Feng, Mann, Matthias

Issue&Volume: 2025-10-21

Abstract: The scale of data generated for mass-spectrometry-based proteomics and modern acquisition strategies poses a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data-independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm that performs machine learning directly on the raw signal and is particularly suited for detecting patterns in data produced by time-of-flight instruments. Benchmarking demonstrates competitive identification and quantification performance. While the method supports empirical spectral libraries, we propose a search strategy named DIA transfer learning that uses fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine-specific and experiment-specific properties, enabling the generic DIA analysis of any post-translational modification. AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

DOI: 10.1038/s41587-025-02791-w

Source: https://www.nature.com/articles/s41587-025-02791-w