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关于复杂混合物中的蛋白质丰度分布。

On protein abundance distributions in complex mixtures.

机构信息

The Scripps Research Institute, 10550 N Torrey Pines Road, La Jolla, CA, 92037, USA.

出版信息

Proteome Sci. 2013 Jan 29;11(1):5. doi: 10.1186/1477-5956-11-5.

Abstract

Mass spectrometry, an analytical technique that measures the mass-to-charge ratio of ionized atoms or molecules, dates back more than 100 years, and has both qualitative and quantitative uses for determining chemical and structural information. Quantitative proteomic mass spectrometry on biological samples focuses on identifying the proteins present in the samples, and establishing the relative abundances of those proteins. Such protein inventories create the opportunity to discover novel biomarkers and disease targets. We have previously introduced a normalized, label-free method for quantification of protein abundances under a shotgun proteomics platform (Griffin et al., 2010). The introduction of this method for quantifying and comparing protein levels leads naturally to the issue of modeling protein abundances in individual samples. We here report that protein abundance levels from two recent proteomics experiments conducted by the authors can be adequately represented by Sichel distributions. Mathematically, Sichel distributions are mixtures of Poisson distributions with a rather complex mixing distribution, and have been previously and successfully applied to linguistics and species abundance data. The Sichel model can provide a direct measure of the heterogeneity of protein abundances, and can reveal protein abundance differences that simpler models fail to show.

摘要

质谱分析是一种测量离子化原子或分子的质荷比的分析技术,已有 100 多年的历史,可用于定性和定量确定化学和结构信息。对生物样本进行定量蛋白质组学质谱分析的重点在于鉴定样本中存在的蛋白质,并确定这些蛋白质的相对丰度。这些蛋白质组学研究为发现新的生物标志物和疾病靶标提供了机会。我们之前曾介绍过一种基于 shotgun 蛋白质组学平台的标准化、无标记定量蛋白质丰度的方法(Griffin 等人,2010 年)。该方法用于定量和比较蛋白质水平,自然而然地引出了在单个样本中对蛋白质丰度进行建模的问题。我们在这里报告,作者最近进行的两项蛋白质组学实验中的蛋白质丰度水平可以用 Sichel 分布充分表示。从数学上讲,Sichel 分布是泊松分布的混合物,具有相当复杂的混合分布,并且之前已经成功应用于语言学和物种丰度数据。Sichel 模型可以直接衡量蛋白质丰度的异质性,并可以揭示简单模型无法显示的蛋白质丰度差异。

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