Yanofsky Corey M, Bell Alexander W, Lesimple Souad, Morales Frank, Lam Tukiet T, Blakney Greg T, Marshall Alan G, Carrillo Brian, Lekpor Kossi, Boismenu Daniel, Kearney Robert E
Bioinformatics Group, Department of Biomedical Engineering, McGill University, Strathcona Building, Montreal, Quebec, Canada.
Anal Chem. 2005 Nov 15;77(22):7246-54. doi: 10.1021/ac050640q.
In high-throughput proteomics, a promising current approach is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) of tryptic peptides from complex mixtures of proteins. To apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error expected in the measured masses. Here, we analyze by LC-FTICR-MS a complex mixture of peptides derived from a sample previously characterized by LC-QTOF-MS. Application of a Bayesian probability model of the data and partial knowledge of the composition of the sample suffice to estimate both the systematic and random errors in measured masses.
在高通量蛋白质组学中，当前一种很有前景的方法是利用液相色谱与傅里叶变换离子回旋共振质谱联用技术（LC-FTICR-MS）分析蛋白质复杂混合物中的胰蛋白酶肽段。要应用此方法，有必要考虑任何系统测量误差，并且估计测量质量中预期的随机误差也很有用。在此，我们通过LC-FTICR-MS分析了源自先前用LC-QTOF-MS表征过的样品的肽段复杂混合物。应用数据的贝叶斯概率模型以及对样品组成的部分了解足以估计测量质量中的系统误差和随机误差。
