类RNA基序的宇宙有多大？利用拓扑描述符对RNA图基序进行聚类分析
How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological Descriptors
AI总结：
该研究结合计算拓扑、PSG特征与无监督聚类，对2至9顶点的110,667种RNA二维对偶图进行分类，预测46.017%的假设图为类RNA，并揭示子图可分性等拓扑差异。
摘要：
我们提出一种基于计算拓扑的方法，并结合无监督机器学习算法，以估计类RNA图拓扑的数据库规模与内容。具体而言，我们应用图论枚举，生成顶点数在2至9之间的所有110,667种可能的二维对偶图。其中，采用RNA-as-Graphs（RAG）映射方法，仅有0.11%的图对应于约200,000个已知RNA原子片段（收集于2021年）。其余99.89%的对偶图可能是类RNA的，也可能是非类RNA的。为确定99.89%假设集合中哪些对偶图更可能与RNA结构相关，我们使用Persistent Spectral Graphs（PSG，持久谱图）方法计算拓扑描述符，用19个基于PSG的特征刻画每个图，并采用聚类算法将所有可能的对偶图划分为两个簇：类RNA簇和非类RNA簇。每个对偶图到类RNA簇中心的距离表示其属于RNA结构的可能性。验证结果表明，我们基于PSG的类RNA簇包含121个已知RNA对偶图中的97.3%，显示出良好性能。此外，46.017%的假设RNA被预测为类RNA。值得注意的是，我们观察到排名前15的类RNA对偶图均可分离为多个子图，而排名前15的非类RNA对偶图往往没有任何子图。此外，在比较拓扑特征时，顶级类RNA图与非类RNA图之间存在显著的拓扑差异。这些发现为认识RNA基序宇宙的规模和RNA设计策略提供了宝贵洞见，为预测RNA图拓扑提供了一个新颖框架，并可指导新型RNA基序的发现，或许还能通过子图组装助力抗病毒治疗。
英文摘要：
We introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. Specifically, we apply graph theory enumeration to generate all 110,667 possible 2D dual graphs for vertex numbers ranging from 2 to 9. Among them, only 0.11% graphs correspond to approximately 200,000 known RNA atomic fragments (collected in 2021) using the RNA-as-Graphs (RAG) mapping method. The remaining 99.89% of the dual graphs may be RNA-like or non-RNA-like. To determine which dual graphs in the 99.89% hypothetical set are more likely to be associated with RNA structures, we apply computational topology descriptors using the Persistent Spectral Graphs (PSG) method to characterize each graph using 19 PSG-based features and use clustering algorithms that partition all possible dual graphs into two clusters, RNA-like cluster and non-RNA-like cluster. The distance of each dual graph to the center of the RNA-like cluster represents the likelihood of it belonging to RNA structures. From validation, our PSG-based RNA-like cluster includes 97.3% of the 121 known RNA dual graphs, suggesting good performance. Furthermore, 46.017% of the hypothetical RNAs are predicted to be RNA-like. Significantly, we observe that all the top 15 RNA-like dual graphs can be separated into multiple subgraphs, whereas the top 15 non-RNA-like dual graphs tend not to have any subgraphs. Moreover, a significant topological difference between top RNA-like and non-RNA-like graphs is evident when comparing their topological features. These findings provide valuable insights into the size of the RNA motif universe and RNA design strategies, offering a novel framework for predicting RNA graph topologies and guiding the discovery of novel RNA motifs, perhaps anti-viral therapeutics by subgraph assembly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
