Semi-supervised LC/MS alignment for differential proteomics
Semi-supervised LC/MS alignment for differential proteomics
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DOI：
10.1093/bioinformatics/btl219
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发表时间：
2006-07-01
期刊：
影响因子：
5.8
通讯作者：
Buhmann, Joachim M.
中科院分区：
文献类型：
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作者：
Fischer, Bernd;Grossmann, Jonas;Buhmann, Joachim M.
动机：质谱（MS）与高效液相色谱（LC）相结合的高通量蛋白质组学分析受到了广泛的关注。同位素标记技术，如ICAT[5,6]已经成功地应用于两种蛋白质样品的差异定量信息，但代价是实验设置的复杂性大大增加。为了克服这些限制，我们考虑一个无标签设置，其中两个样品的元素之间的对应关系必须在比较分析之前建立。通过非线性鲁棒脊回归实现样本间的对齐。对应估计以半监督的方式由序列串联质谱的先验信息指导。结果：发现对应关系的半监督方法成功地应用于高度复杂的蛋白质样品，即使它们由于不同的生物条件而表现出很大的变化。大规模实验清楚地表明，所提出的方法弥合了统计数据分析和无标记的定量差异蛋白质组学之间的差距。
Motivation: Massspectrometry ( MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra.Results: The semi-supervised method for finding correspondences was successfully applied to aligning highly complex protein samples, even if they exhibit large variations due to different biological conditions. A large-scale experiment clearly demonstrates that the proposed method bridges the gap between statistical data analysis and label-free quantitative differential proteomics.