Hardouin Pierre, Pan Nan, Lyonnet du Moutier Francois-Xavier, Chamond Nathalie, Ponty Yann, Will Sebastian, Sargueil Bruno
CNRS UMR 8038, CiTCoM Cibles Thérapeutiques et Conception de Médicaments, Université Paris Cité, 4 avenue de l'Observatoire, 75270 Paris, France.
CNRS UMR 7161, LIX, Ecole Polytechnique, 1 rue Estienne d'Orves, 91120 Palaiseau, France.
NAR Genom Bioinform. 2025 Mar 25;7(1):lqaf028. doi: 10.1093/nargab/lqaf028. eCollection 2025 Mar.
In addition to their sequence, multiple functions of RNAs are encoded within their structure, which is often difficult to solve using physico-chemical methods. Incorporating low-resolution experimental data such as chemical probing into computational prediction significantly enhances RNA structure modeling accuracy. While medium- and high-throughput RNA structure probing techniques are widely accessible, the subsequent analysis process can be cumbersome, involving multiple software and manual data manipulation. In addition, the relevant interpretation of the data requires proper parameterization of the software and a strict consistency in the analysis pipeline. To streamline such workflows, we introduce IPANEMAP Suite, a comprehensive platform that guides users from chemically probing raw data to visually informative secondary structure models. IPANEMAP Suite seamlessly integrates various experimental datasets and facilitates comparative analysis of RNA structures under different conditions (footprinting), aiding in the study of protein or small molecule interactions with RNA. Here, we show that the unique ability of IPANEMAP Suite to perform integrative modeling using several chemical probing datasets with phylogenetic data can be instrumental in obtaining accurate secondary structure models. The platform's project-based approach ensures full traceability and generates publication-quality outputs, simplifying the entire RNA structure analysis process. IPANEMAP Suite is freely available at https://github.com/Sargueil-CiTCoM/ipasuite under a GPL-3.0 license.
除了序列之外,RNA的多种功能还编码在其结构中,而使用物理化学方法往往难以解析这种结构。将化学探针等低分辨率实验数据纳入计算预测,可显著提高RNA结构建模的准确性。虽然中高通量RNA结构探测技术已广泛可用,但后续分析过程可能很繁琐,涉及多个软件和手动数据处理。此外,数据的相关解读需要对软件进行适当的参数设置,并在分析流程中保持严格的一致性。为了简化此类工作流程,我们推出了IPANEMAP套件,这是一个综合平台,可指导用户从化学探测原始数据到获得具有视觉信息的二级结构模型。IPANEMAP套件无缝集成了各种实验数据集,并便于对不同条件下的RNA结构进行比较分析(足迹分析),有助于研究蛋白质或小分子与RNA的相互作用。在此,我们表明,IPANEMAP套件利用多个化学探测数据集和系统发育数据进行整合建模的独特能力,有助于获得准确的二级结构模型。该平台基于项目的方法确保了完全可追溯性,并生成可用于发表的高质量输出,简化了整个RNA结构分析过程。IPANEMAP套件在GPL-3.0许可下可从https://github.com/Sargueil-CiTCoM/ipasuite免费获取。