当前位置:科学网首页 > 小柯机器人 >详情
自动化数据完善的DIA数据管理工具
作者:小柯机器人 发布时间:2020/11/17 16:01:51

美国麻省理工学院和哈佛大学Jacob D. Jaffe、Alvaro Sebastian Vaca Jacome小组取得新进展,他们将Avant-garde作为自动化数据完善的数据非依赖采集(DIA)管理工具。2020年11月16日出版的《自然-方法学》发表了这一研究成果。

研究人员将Avant-garde作为完善DIA(和平行反应监测)数据的工具。Avant-garde使用新颖的数据驱动评分策略:通过从挖掘数据集本身来优化信号,并使用所有样本中的测量值来实现最佳优化。研究人员使用基准DIA数据集来评估Avant-garde的性能,表明它在确定肽段峰度中的适用性,并可达到与手动验证相同的选择性、准确性和可重复性。 Avant-garde是现有DIA分析工具的补充,旨在为定量质谱数据的后续分析奠定坚实的基础。

研究人员表示,DIA数据分析中仍然存在一些难题,例如要准确地识别肽段、定义整合边界、消除干扰并控制错误的发现率。实际上,DIA仍然需要对信号进行人工检查,这对于分析大型数据集是不切实际的。

附:英文原文

Title: Avant-garde: an automated data-driven DIA data curation tool

Author: Alvaro Sebastian Vaca Jacome, Ryan Peckner, Nicholas Shulman, Karsten Krug, Katherine C. DeRuff, Adam Officer, Karen E. Christianson, Brendan MacLean, Michael J. MacCoss, Steven A. Carr, Jacob D. Jaffe

Issue&Volume: 2020-11-16

Abstract: Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data. A computational tool, Avant-garde, automates refinement of data-independent acquisition mass spectrometry-based quantitative proteomics data.

DOI: 10.1038/s41592-020-00986-4

Source: https://www.nature.com/articles/s41592-020-00986-4

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex