Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, 90095, USA.
Department of Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
Nat Commun. 2019 Mar 22;10(1):1338. doi: 10.1038/s41467-019-09292-w.
Allele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants (GVs) mediating post-transcriptional regulation. Recently, genome-wide detection of in vivo binding of RNA-binding proteins is greatly facilitated by the enhanced crosslinking and immunoprecipitation (eCLIP) method. We developed a new computational approach, called BEAPR, to identify allele-specific binding (ASB) events in eCLIP-Seq data. BEAPR takes into account crosslinking-induced sequence propensity and variations between replicated experiments. Using simulated and actual data, we show that BEAPR largely outperforms often-used count analysis methods. Importantly, BEAPR overcomes the inherent overdispersion problem of these methods. Complemented by experimental validations, we demonstrate that the application of BEAPR to ENCODE eCLIP-Seq data of 154 proteins helps to predict functional GVs that alter splicing or mRNA abundance. Moreover, many GVs with ASB patterns have known disease relevance. Overall, BEAPR is an effective method that helps to address the outstanding challenge of functional interpretation of GVs.
等位基因特异性蛋白-RNA 结合是一个重要的方面，它可能揭示介导转录后调控的功能遗传变异 (GVs)。最近，通过增强交联和免疫沉淀 (eCLIP) 方法，大大促进了全基因组范围内对 RNA 结合蛋白体内结合的检测。我们开发了一种名为 BEAPR 的新计算方法，用于识别 eCLIP-Seq 数据中的等位基因特异性结合 (ASB) 事件。BEAPR 考虑了交联诱导的序列倾向和重复实验之间的差异。使用模拟和实际数据，我们表明 BEAPR 在很大程度上优于常用的计数分析方法。重要的是，BEAPR 克服了这些方法固有的过度分散问题。通过实验验证补充，我们证明将 BEAPR 应用于 ENCODE 的 154 种蛋白质的 eCLIP-Seq 数据有助于预测改变剪接或 mRNA 丰度的功能 GVs。此外，许多具有 ASB 模式的 GVs 与已知的疾病相关。总的来说，BEAPR 是一种有效的方法，可以帮助解决 GVs 功能解释的突出挑战。
