Center for RNA Systems Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
Cell Syst. 2017 May 24;4(5):568-574.e7. doi: 10.1016/j.cels.2017.04.007. Epub 2017 May 10.
A number of sequencing-based transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and RNA modification. Although these assays survey a diverse set of epitranscriptomic marks, we use the term toeprinting assays since they share methodological similarities. Their interpretation is predicated on addressing a similar computational challenge: how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. We introduce PROBer, a statistical model and associated software, that addresses this challenge for the analysis of toeprinting assays. PROBer takes sequencing data as input and outputs estimated transcript abundances and isoform-specific modification profiles. Results on both simulated and biological data demonstrate that PROBer significantly outperforms individual methods tailored for specific toeprinting assays. Since the space of toeprinting assays is ever expanding and these assays are likely to be performed and analyzed together, we believe PROBer's unified data analysis solution will be valuable to the RNA community.
近年来，已经开发了许多基于测序的转录本脱落测定法，用于探测 RNA-蛋白质相互作用、RNA 结构和 RNA 修饰的转录后动力学。尽管这些测定法调查了一组不同的表观转录组标记，但由于它们具有相似的方法学相似性，我们将其称为足迹印迹测定法。它们的解释取决于解决类似的计算挑战：如何在复杂的读取多重映射情况下学习同种型特异性的化学修饰谱。我们引入了 PROBer，这是一种统计模型和相关软件，可用于分析足迹印迹测定法。PROBer 将测序数据作为输入，并输出估计的转录物丰度和同种型特异性修饰谱。模拟和生物学数据的结果表明，PROBer 显著优于针对特定足迹印迹测定法定制的单个方法。由于足迹印迹测定法的范围不断扩大，并且这些测定法很可能一起进行和分析，因此我们相信 PROBer 的统一数据分析解决方案对 RNA 社区将具有重要价值。
