Chapter 7. Algorithms for MS1-Based Quantitation

Hanqing Liao, Alexander Phillips, A. Jankevics, A. Dowsey
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Abstract

MS1-based quantitation is performed by direct integration of peptide precursor signal intensity from the MS1 spectra across retention time, based on the assumption that these signals have a linear relationship with abundance across a relatively wide dynamic range. Since ionisation efficiency varies between peptides, only relative abundance changes between biological samples are usually established. Whether each sample is run individually ‘label-free’, or two or three samples multiplexed within each run by a MS1-based labelling technique such as stable isotope labeling by amino acids in cell culture (SILAC), the informatics methods involved are broadly similar. In this chapter we present the key components of such pipelines, including the detection and quantitation of peptide features from the raw data, alignment of chromatographic variations between runs so that corresponding features can be matched, intensity normalisation to correct sample-loading differences and ionisation fluctuations, and methods to combine the peptide-level quantifications for the statistical analysis of differential protein expression across treatment groups. At each stage, the techniques have been designed for robustness against the systematic and random variation inherent in MS data, and errors during the preceding parts of the pipeline.
第七章。基于ms1的定量算法
基于MS1的定量是基于假设这些信号在相对宽的动态范围内与丰度呈线性关系,通过MS1光谱中肽前体信号强度随保留时间的直接积分来完成的。由于多肽之间的电离效率不同,通常只确定生物样品之间的相对丰度变化。无论每个样品是单独“无标记”运行,还是在每次运行中使用基于ms1的标记技术(如细胞培养中氨基酸的稳定同位素标记(SILAC))将两个或三个样品进行多路处理,所涉及的信息学方法大致相似。在本章中,我们介绍了这些管道的关键组成部分,包括从原始数据中检测和定量肽特征,在运行之间对色谱变化进行校准,以便匹配相应的特征,强度归一化以纠正样品装载差异和电离波动,以及结合肽水平定量以统计分析处理组间差异蛋白表达的方法。在每个阶段,这些技术都被设计为对MS数据固有的系统和随机变化的鲁棒性,以及管道前部分的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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