Identifying technical aliases in SELDI mass spectra of complex mixtures of proteins.
Identifying technical aliases in SELDI mass spectra of complex mixtures of proteins.
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DOI：
10.1186/1756-0500-6-358
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发表时间：
2013-09-08
影响因子：
1.8
通讯作者：
Cohen HJ
中科院分区：
文献类型：
--
作者：
Whitin JC;Rangan S;Cohen HJ
使用蛋白质的复杂混合物的质谱蛋白质分析创建的生物标志物发现数据集包含许多代表具有不同电荷状态的相同蛋白质的峰。诸如此类的相关变量会混淆蛋白质组学数据的统计分析。以前，我们开发了一种算法，聚类生物学或技术相关的质谱峰。在这里，我们展示了一个算法，集群相关的技术别名。在本文中，我们提出了一种预处理算法，可用于分组技术别名在质谱蛋白质分析数据。允许聚类的方差的严格性是可定制的，从而影响聚类的峰的数量。随后对聚类而不是单个峰的分析有助于减少与技术相关数据相关的困难，并且可以帮助更有效地识别生物标志物。该软件可用于预处理，从而降低蛋白质谱蛋白质组学数据的复杂性，从而通过减少测试次数来简化后续的生物标志物分析。该软件也是一个实用的工具，用于确定哪些特征需要通过纯化、鉴定和确认进一步研究。
Biomarker discovery datasets created using mass spectrum protein profiling of complex mixtures of proteins contain many peaks that represent the same protein with different charge states. Correlated variables such as these can confound the statistical analyses of proteomic data. Previously we developed an algorithm that clustered mass spectrum peaks that were biologically or technically correlated. Here we demonstrate an algorithm that clusters correlated technical aliases only. In this paper, we propose a preprocessing algorithm that can be used for grouping technical aliases in mass spectrometry protein profiling data. The stringency of the variance allowed for clustering is customizable, thereby affecting the number of peaks that are clustered. Subsequent analysis of the clusters, instead of individual peaks, helps reduce difficulties associated with technically-correlated data, and can aid more efficient biomarker identification. This software can be used to pre-process and thereby decrease the complexity of protein profiling proteomics data, thus simplifying the subsequent analysis of biomarkers by decreasing the number of tests. The software is also a practical tool for identifying which features to investigate further by purification, identification and confirmation.