MapQuant: Open-source software for large-scale protein quantification
MapQuant: Open-source software for large-scale protein quantification
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DOI：
10.1002/pmic.200500201
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发表时间：
2006-03-01
期刊：
影响因子：
3.4
通讯作者：
Church, GM
中科院分区：
文献类型：
--
作者：
Leptos, KC;Sarracino, DA;Church, GM
使用MS进行全细胞蛋白质定量已被证明是一项具有挑战性的任务。检测效率在肽与肽之间变化很大，分子身份先验不明显，并且肽在整个多维数据空间中不均匀地分散。为了克服这些挑战，我们开发了一个开源软件包，MapQuant，全面量化在大型MS数据集中检测到的有机物种。MapQuant将LC/MS实验视为图像，并利用标准图像处理技术进行噪声过滤、分水岭分割、峰查找、峰拟合、峰聚类、电荷状态测定和碳含量估计。MapQuant报告的丰度值与低分辨率和高分辨率仪器上分析的样品量呈线性关系（超过1000倍动态范围）。添加到样品中的背景噪声，无论是作为一个中等复杂性的肽混合物或作为一个高复杂性的胰蛋白酶化的蛋白质组，发挥MapQuant报告的丰度值的影响可以忽略不计，与其他方法的变异系数。最后，MapQuant在高分辨率质谱仪上定义同位素尘埃的准确质量和保留时间特征的能力可以通过将序列身份分配给观察到的同位素尘埃而无需相应的MS/MS数据来增加蛋白质序列覆盖率。
Whole-cell protein quantification using MS has proven to be a challenging task. Detection efficiency varies significantly from peptide to peptide, molecular identities are not evident a priori, and peptides are dispersed unevenly throughout the multidimensional data space. To overcome these challenges we developed an open-source software package, MapQuant, to quantify comprehensively organic species detected in large MS datasets. MapQuant treats an LC/MS experiment as an image and utilizes standard image processing techniques to perform noise filtering, watershed segmentation, peak finding, peak fitting, peak clustering, charge-state determination and carbon-content estimation. MapQuant reports abundance values that respond linearly with the amount of sample analyzed on both low- and high-resolution instruments (over a 1000-fold dynamic range). Background noise added to a sample, either as a medium-complexity peptide mixture or as a high-complexity trypsinized proteome, exerts negligible effects on the abundance values reported by MapQuant and with coefficients of variance comparable to other methods. Finally, MapQuant's ability to define accurate mass and retention time features of isotopic dusters on a high-resolution mass spectrometer can increase protein sequence coverage by assigning sequence identities to observed isotopic dusters without corresponding MS/MS data.