华盛顿大学William Stafford Noble课题组在研究中取得进展。他们报道了用于数据独立采集质谱数据从头测序的变压器模型。相关论文于2025年7月1日发表在《自然—方法学》杂志上。

因此，课题组提出了一个名为Cascadia的从头排序模型，该模型采用了一个转换架构来处理由DIA协议生成的更复杂的数据。与现有的DIA数据从头测序方法相比，Cascadia在一系列仪器和实验方案中实现了显着提高的性能。

据介绍，质谱数据分析中的一个核心计算挑战是从头测序问题，其中产生的氨基酸序列直接从观察到的片段谱中推断出来，而没有序列数据库的主题。最近，深度学习模型通过从大量高置信度标记质谱数据集中学习，在从头测序方面取得了实质性进展。然而，这些方法主要是为数据依赖的采集实验而设计的。在过去的十年中，质谱领域由于其优越的特异性和可重复性，一直朝着数据独立采集（DIA）方案的方向发展，用于分析复杂的蛋白质组学样品。

附：英文原文

Title: A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data

Author: Sanders, Justin, Wen, Bo, Rudnick, Paul A., Johnson, Richard S., Wu, Christine C., Riffle, Michael, Oh, Sewoong, MacCoss, Michael J., Noble, William Stafford

Issue&Volume: 2025-07-01

Abstract: A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem, in which the generating amino acid sequence is inferred directly from an observed fragmentation spectrum without the use of a sequence database. Recently, deep learning models have made substantial advances in de novo sequencing by learning from massive datasets of high-confidence labeled mass spectra. However, these methods are designed primarily for data-dependent acquisition experiments. Over the past decade, the field of mass spectrometry has been moving toward using data-independent acquisition (DIA) protocols for the analysis of complex proteomic samples owing to their superior specificity and reproducibility. Hence, we present a de novo sequencing model called Cascadia, which uses a transformer architecture to handle the more complex data generated by DIA protocols. In comparisons with existing approaches for de novo sequencing of DIA data, Cascadia achieves substantially improved performance across a range of instruments and experimental protocols.

DOI: 10.1038/s41592-025-02718-y

Source: https://www.nature.com/articles/s41592-025-02718-y