Li Xiao-Jun, Zhang Hui, Ranish Jeffrey A, Aebersold Ruedi
The Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103-8904, USA.
Anal Chem. 2003 Dec 1;75(23):6648-57. doi: 10.1021/ac034633i.
We describe an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) of proteins contained in two samples. Proteins are labeled with distinct stable-isotope tags and fragmented, and the tagged peptide fragments are separated by liquid chromatography (LC) and analyzed by electrospray ionization (ESI) tandem mass spectrometry (MS/MS). The algorithm utilizes the signals recorded for the different isotopic forms of peptides of identical sequence and numerical and statistical methods, such as Savitzky-Golay smoothing filters, statistics for weighted samples, and Dixon's test for outliers, to evaluate protein abundance ratios and their associated errors. The algorithm also provides a statistical assessment to distinguish proteins of significant abundance changes from a population of proteins of unchanged abundance. To evaluate its performance, two sets of LC-ESI-MS/MS data were analyzed by the ASAPRatio algorithm without human intervention, and the data were related to the expected and manually validated values. The utility of the ASAPRatio program was clearly demonstrated by its speed and the accuracy of the generated protein abundance ratios and by its capability to identify specific core components of the RNA polymerase II transcription complex within a high background of copurifying proteins.
我们描述了一种用于对两个样本中所含蛋白质的丰度比进行自动统计分析的算法（ASAPRatio）。蛋白质用不同的稳定同位素标签进行标记并裂解，标记的肽片段通过液相色谱（LC）分离，然后通过电喷雾电离（ESI）串联质谱（MS/MS）进行分析。该算法利用针对相同序列肽的不同同位素形式记录的信号以及数值和统计方法，如Savitzky-Golay平滑滤波器、加权样本统计以及用于异常值检测的狄克逊检验，来评估蛋白质丰度比及其相关误差。该算法还提供统计评估，以从丰度未改变的蛋白质群体中区分出丰度有显著变化的蛋白质。为了评估其性能，两组LC-ESI-MS/MS数据在无人为干预的情况下由ASAPRatio算法进行分析，并且这些数据与预期值和人工验证值相关。ASAPRatio程序的实用性通过其速度、所生成的蛋白质丰度比的准确性以及在共纯化蛋白质的高背景中识别RNA聚合酶II转录复合物特定核心成分的能力得到了清晰证明。
