Department of Medicine, Yong Loo Lin School of Medicine , National University of Singapore , Singapore 117599 , Singapore.
Saw Swee Hock School of Public Health , National University of Singapore , Singapore 117549 , Singapore.
J Proteome Res. 2019 Feb 1;18(2):748-752. doi: 10.1021/acs.jproteome.8b00483. Epub 2018 Nov 19.
We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.
我们介绍了 EBprotV2,这是一种用于基于标记的蛋白质组学实验中基于肽比的差异蛋白质丰度分析的 Perseus 插件。EBprot 的原始版本将对数转换后的肽比分布建模为差异丰度蛋白和非差异丰度蛋白的高斯混合体,并根据肽比的可重现幅度计算每个蛋白的差异丰度概率得分。然而,完全参数模型可能不够灵活,并且其 R 实现对于包含大量肽(例如 >100,000)的数据集来说计算时间很长。用 C++语言构建的新工具不仅计算时间更快,而且还配备了一个灵活的半参数模型,可以更好地处理偏态比分布。我们还为 EBprotV2 开发了一个 Perseus 插件,以便于使用该工具。此外,该工具现在提供了一个新的子模块(MakeGrpData),可将无标记肽强度数据转换为用于组比较的肽比数据,并使用混合模型进行差异丰度分析。当无标记数据有许多缺失的肽强度数据点时,这种方法尤其有用。