Fischer Bernd, Grossmann Jonas, Roth Volker, Gruissem Wilhelm, Baginsky Sacha, Buhmann Joachim M
Institute of Computational Science, ETH Zurich, Switzerland.
Bioinformatics. 2006 Jul 15;22(14):e132-40. doi: 10.1093/bioinformatics/btl219.
Mass spectrometry (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.
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.
The software will be available on the website http://people.inf.ethz.ch/befische/proteomics.
质谱（MS）与高效液相色谱（LC）相结合在蛋白质组高通量分析方面受到了广泛关注。诸如ICAT [5,6]等同位素标记技术已成功应用于获取两个蛋白质样品的差异定量信息，然而代价是实验设置的复杂性显著增加。为克服这些限制，我们考虑一种无标记设置，即在进行比较分析之前必须先建立两个样品元素之间的对应关系。样品之间的比对通过非线性稳健岭回归实现。对应关系估计以半监督方式由从串联质谱序列中获得的先验信息引导。
用于寻找对应关系的半监督方法已成功应用于高度复杂蛋白质样品的比对，即使这些样品因不同生物学条件而表现出很大差异。一项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与无标记定量差异蛋白质组学之间的差距。
