基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

Advances of peptide-centric data-independent acquisition analysis algorithms and software tools

摘要:

数据非依赖采集(data-independent acquisition, DIA)是一种高通量、无偏性的质谱数据采集方法，具有定量结果重现性好，对低丰度蛋白质友好的特点，是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱，包含了多个肽段的碎片离子信息，使得蛋白质鉴定和定量更加困难。目前，DIA数据分析方法分为两大类，即以肽为中心和以谱图为中心。其中，以肽为中心的分析方法鉴定更灵敏，定量更准确，已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程，介绍了基于此流程的数据分析软件及相关比较评估工作，进一步总结了已有的算法改进工作，最后对未来发展方向进行了展望。

Abstract:

Data-independent acquisition (DIA) is a high-throughput, unbiased mass spectrometry data acquisition method which has good quantitative reproducibility and is friendly to low-abundance proteins. It becomes the preferred choice for clinical proteomic studies especially for large cohort studies in recent years. The mass-spectrometry (MS)/MS spectra generated by DIA is usually heavily mixed with fragment ion information of multiple peptides, which makes the protein identification and quantification more difficult. Currently, DIA data analysis methods fall into two main categories, namely peptide-centric and spectrum-centric. The peptide-centric strategy is more sensitive for identification and more accurate for quantification. Thus, it has become the mainstream strategy for DIA data analysis, which includes four key steps: building a spectral library, extracting ion chromatogram, feature scoring and statistical quality control. This work reviews the peptide-centric DIA data analysis procedure, introduces the corresponding algorithms and software tools, and summarizes the improvements for the existing algorithms. Finally, the future development directions are discussed.