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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >DESIGN AND ANALYSIS OF QUANTITATIVE DIFFERENTIAL PROTEOMICS INVESTIGATIONS USING LC-MS TECHNOLOGY
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DESIGN AND ANALYSIS OF QUANTITATIVE DIFFERENTIAL PROTEOMICS INVESTIGATIONS USING LC-MS TECHNOLOGY

机译:基于LC-MS技术的定量差异蛋白质组学研究的设计与分析

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Liquid chromatography–mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS–based quantitation isto be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS–based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.
机译:基于液相色谱-质谱(LC-MS)的蛋白质组学正在成为表征各种类型和跨条件的生物样品中蛋白质丰度的越来越重要的工具。疾病或药物治疗对蛋白质丰度的影响对于生物学过程的表征和生物标志物的鉴定尤为重要。尽管可以使用最先进的仪器进行高质量的测量,并且可以使用市售的软件来处理数据,但是技术和数据的复杂性给生物信息学家和统计学家带来了挑战。在这里,我们描述了用于定量LC-MS数据分析的管道。该管道的关键组成部分包括实验设计(样品池,封闭和随机化),以及质谱的去卷积和对齐以生成分子丰度分布图矩阵。基于LC-MS的定量分析的一个重要挑战是如何准确识别蛋白质家族成员的丰度测量值并将其分配给它们。为了解决这个问题,我们实现了一种新颖的统计方法,可以从胰蛋白酶解肽的强度推断蛋白质家族相关成员的相对丰度。该管道已用于分析来自多个生物标记物发现项目的定量LC-MS数据。我们在此以其中两项研究为例来说明我们的管道,并表明该管道构成了基于LC-MS的差分定量分析的完整可行框架。补充材料可从http://iec01.mie.utoronto.ca/~thodoros/Bukhman/获得。

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