英国弗朗西斯-克里克研究所Markus Ralser研究组开发出一个神经网络和干扰校正工具，其能够在高通量下实现对蛋白组学的深度覆盖。2019年11月25日《自然—方法学》在线发表了这项成果。

研究人员报道了一个易于使用的集成软件套件DIA-NN，其利用深度神经网络以及新的量化和信号校正策略来处理与数据无关的获取（DIA）蛋白质组学实验。DIA-NN提高了常规DIA蛋白质组学应用程序中的鉴定和定量性能，尤其适用于高通量应用，因为它与快速色谱方法结合使用时速度快，并能实现深度和可靠的蛋白质组覆盖。

附：英文原文

Title: DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

Author: Vadim Demichev, Christoph B. Messner, Spyros I. Vernardis, Kathryn S. Lilley, Markus Ralser

Issue&Volume: 2019-11-25

Abstract: We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

DOI: 10.1038/s41592-019-0638-x

Source: https://www.nature.com/articles/s41592-019-0638-x