小柯机器人

德国马克斯-普朗克生物化学研究所Jrgen Cox小组开发出新方法MaxDIA，可实现基于文库和无文库的数据独立采集蛋白质组学。2021年7月8日，《自然—生物技术》杂志在线发表了这项成果。

研究人员开发了MaxDIA，这是一个软件平台，用于在MaxQuant软件环境中分析数据独立采集（DIA）蛋白质组学数据。使用光谱库，MaxDIA实现了深度蛋白质组覆盖，蛋白质定量的变异系数比其他软件要好得多。MaxDIA能够在不同水平进行准确的错误发现率（FDR）估计，包括使用全蛋白质组预测光谱库时。这是发现DIA的基础：无需文库和可靠的FDR控制即可对DIA样品进行无假设分析。

MaxDIA对片段数据进行3维或4维特征检测，并且匹配的评分通过机器学习对识别特征进行增强。MaxDIA的bootstrap DIA工作流程执行多轮匹配，可提高重新校准的质量和匹配库的严格性。将MaxDIA与BoxCar采集和俘获离子迁移谱仪这两项新技术相结合，均可实现深度和准确的蛋白质组定量。

附：英文原文

Title: MaxDIA enables library-based and library-free data-independent acquisition proteomics

Author: Pavel Sinitcyn, Hamid Hamzeiy, Favio Salinas Soto, Daniel Itzhak, Frank McCarthy, Christoph Wichmann, Martin Steger, Uli Ohmayer, Ute Distler, Stephanie Kaspar-Schoenefeld, Nikita Prianichnikov, ule Ylmaz, Jan Daniel Rudolph, Stefan Tenzer, Yasset Perez-Riverol, Nagarjuna Nagaraj, Sean J. Humphrey, Jrgen Cox

Issue&Volume: 2021-07-08

Abstract: MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.

DOI: 10.1038/s41587-021-00968-7

Source: https://www.nature.com/articles/s41587-021-00968-7

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex