[以为中心的数据独立采集分析算法和软件工具的进展]
Yingying Zhang1,2, Kunxian Shu1, Cheng Chang2
1Chongqing Key Laboratory of Big Data for Bio-intelligence, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
|October 8, 2023
概括
数据独立采集 (DIA) 质谱为临床蛋白质组学提供可重复量化的量化. 本综述详细介绍了以为中心的方法,提高了复杂样本中蛋白质识别和量化准确度.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 质谱测量质量谱测量
- 生物技术是生物技术.
背景情况:
- 数据独立采集 (DIA) 是一种高通量,无偏向的质谱法,在临床蛋白质学中受到青,因为它具有定量可重复性和对低丰度蛋白质的敏感性.
- DIA产生复杂的MS/MS光谱,对精确的蛋白质识别和定量提出了挑战.
- 目前的DIA数据分析方法被广泛分为以为中心和以频谱为中心的策略.
研究的目的:
- 审查用于DIA质谱的以为中心的数据分析程序.
- 介绍用于基 DIA 分析的算法和软件工具.
- 总结现有算法的改进,并讨论未来的方向.
主要方法:
- 对以基为中心的DIA数据分析工作流程的审查.
- 相关算法和软件的识别和总结.
- 对算法改进和未来趋势的分析.
主要成果:
- 这种以为中心的策略为蛋白质识别提供了更高的灵敏度,并为DIA数据的量化提供了更高的准确性.
- 基分析的关键步骤包括光谱图书馆建设,离子色谱提取,特征评分和统计质量控制.
- 现有的算法已经得到了改进,增强了整体分析管道.
结论:
- 由于其性能,以基为中心的策略是DIA数据分析的主流方法.
- 持续开发算法和工具对于推进DIA蛋白质组学至关重要.
- 本综述为以为中心的DIA数据分析提供了全面的概述和前景.
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