CNRS UMR 7161, LIX, Ecole Polytechnique, Institut Polytechnique de Paris, 1 rue Estienne d'Orves, 91120 Palaiseau, France.
CNRS UMR 8038, CitCoM, Université de Paris, 4 avenue de l'observatoire, 75006 Paris, France.
Nucleic Acids Res. 2020 Sep 4;48(15):8276-8289. doi: 10.1093/nar/gkaa607.
The manual production of reliable RNA structure models from chemical probing experiments benefits from the integration of information derived from multiple protocols and reagents. However, the interpretation of multiple probing profiles remains a complex task, hindering the quality and reproducibility of modeling efforts. We introduce IPANEMAP, the first automated method for the modeling of RNA structure from multiple probing reactivity profiles. Input profiles can result from experiments based on diverse protocols, reagents, or collection of variants, and are jointly analyzed to predict the dominant conformations of an RNA. IPANEMAP combines sampling, clustering and multi-optimization, to produce secondary structure models that are both stable and well-supported by experimental evidences. The analysis of multiple reactivity profiles, both publicly available and produced in our study, demonstrates the good performances of IPANEMAP, even in a mono probing setting. It confirms the potential of integrating multiple sources of probing data, informing the design of informative probing assays.
从化学探测实验中手动生成可靠的 RNA 结构模型受益于整合来自多个方案和试剂的信息。然而,对多个探测谱的解释仍然是一项复杂的任务,阻碍了建模工作的质量和可重复性。我们引入了 IPANEMAP,这是第一个用于从多个探测反应性谱中构建 RNA 结构模型的自动化方法。输入谱可以来自基于不同方案、试剂或变体集合的实验,并且可以联合分析以预测 RNA 的主要构象。IPANEMAP 结合了采样、聚类和多优化,以产生既稳定又有实验证据支持的二级结构模型。对来自我们研究和公共数据库中的多个反应性谱的分析表明,即使在单探测设置下,IPANEMAP 的性能也很好。它证实了整合多个探测数据来源的潜力,为有信息量的探测实验的设计提供了信息。