Pediatric Biochemistry Laboratory, University of Texas at San Antonio, 78249, USA.
BMC Bioinformatics. 2011 Mar 15;12:74. doi: 10.1186/1471-2105-12-74.
Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.
MRCQuant showed significant improvement in the number of accurately quantified peptides.
MRCQuant effectively addresses major issues in the relative quantification of LC-MS-based proteomics data, and it provides improved performance in the quantification of low abundance peptides.
相对同位素丰度定量可用于肽鉴定和差异肽定量，在基于液相色谱-质谱（LC-MS）的蛋白质组学中起着重要作用。然而，在飞行时间（TOF）仪器上对肽进行相对同位素定量时存在几个主要问题：LC 峰边界检测、热噪声抑制、干扰去除和质量漂移校正。我们提出使用最大比合并（MRC）方法来提取 MS 信号模板以进行干扰检测/去除和 LC 峰边界检测。在我们的方法中，MRCQuant 直接从实验值中提取 MS 模板，并自动捕获和补偿每个 LC-MS 运行中的质量漂移。我们使用从公共数据存储库下载的数据集，将 MRCQuant 的定量准确性与另一种代表性的 LC-MS 定量算法（msInspect）进行了比较。
MRCQuant 在准确定量的肽数量方面有显著提高。
MRCQuant 有效地解决了基于 LC-MS 的蛋白质组学数据相对定量中的主要问题，并在低丰度肽的定量方面提供了改进的性能。
