标题： RNA类似基序的宇宙有多大？ 使用拓扑描述符对RNA图基序进行聚类分析 显示英文标题

标题： How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological Descriptors

摘要： 我们引入了一种基于计算拓扑的方法，结合无监督机器学习算法，以估计RNA类似图拓扑的数据库大小和内容。 具体来说，我们应用图论枚举法生成顶点数从2到9的所有110,667个可能的二维对偶图。 其中，只有0.11%的图通过RNA-as-Graphs（RAG）映射方法对应大约200,000个已知的RNA原子片段（收集于2021年）。 其余的99.89%的对偶图可能是RNA类似或非RNA类似。 为了确定这99.89%假设集中的哪些对偶图更可能与RNA结构相关，我们应用计算拓扑描述符，使用持久谱图（PSG）方法，通过19个基于PSG的特征来表征每个图，并使用聚类算法将所有可能的对偶图分为两个簇，RNA类似簇和非RNA类似簇。 每个对偶图到RNA类似簇中心的距离代表其属于RNA结构的可能性。 从验证结果来看，我们的基于PSG的RNA类似簇包括了121个已知RNA对偶图的97.3%，表明性能良好。 此外，46.017%的假设RNA被预测为RNA类似。 显著的是，我们观察到所有前15个RNA类似对偶图可以被分成多个子图，而前15个非RNA类似对偶图则倾向于没有任何子图。 此外，在比较它们的拓扑特征时，顶级RNA类似和非RNA类似图之间存在显著的拓扑差异。 这些发现为RNA基序宇宙的大小和RNA设计策略提供了有价值的见解，为预测RNA图拓扑提供了一个新的框架，并指导新型RNA基序的发现，也许通过子图组装发现抗病毒治疗药物。 显示英文摘要

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.