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The significance of RNA functions and their role in evolution and disease control have remarkably increased the research scope in the field of RNA science. Though the availability of RNA structure data in PBD has been growing tremendously, maintaining their quality and integrity has become the greater challenge. Since the data available in PDB are results of different independent research, they might contain redundancy. As a result, there remains a possibility of data bias for both protein and RNA chains. Quite a few studies have been conducted to remove the redundancy of protein structures by introducing high-quality representatives. However, the amount of research done to remove the redundancy of RNA structures is still very low. To remove RNA chain redundancy in PDB, we have introduced RNA-NRD, a non-redundant dataset of RNA chains based on sequence and 3D structural similarity. We compared RNA-NRD with the existing non-redundant RNA structure dataset RS-RNA and showed that it has better-formed clusters of redundant RNA chains with lower average RMSD and higher average PSI, thus improving the overall quality of the dataset.

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. While several existing computational methods can help in the analysis of RNA properties by recognizing structural motifs, they do not provide the means to compare and contrast those motifs extensively. We have developed a new method, RNAMotifContrast, which focuses on analyzing the similarities and variations of RNA structural motif characteristics. In this method, a graph is formed to represent the similarities among motifs, and a new traversal algorithm is applied to generate visualizations of their structural properties. Analyzing the structural features among motifs, we have recognized and generalized the concept of motif subfamilies. To asses its effectiveness, we have applied RNAMotifContrast on a dataset of known RNA structural motif families. From the results, we observed that the derived subfamilies possess unique structural variations while holding standard features of the families. Overall, the visualization approach of this method presents a new perspective to observe the relation among motifs more closely, and the discovered subfamilies provide opportunities to achieve valuable insights into RNA’s diverse roles.

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Motivation The 3D structures of RNA play a critical role in understanding their functionalities. There exist several computational methods to study RNA 3D structures by identifying structural motifs and categorizing them into several motif families based on their structures. Although the number of such motif families is not limited, a few of them are well-studied. Out of these structural motif families, there exist several families that are visually similar or very close in structure, even with different base interactions. Alternatively, some motif families share a set of base interactions but maintain variation in their 3D formations. These similarities among different motif families, if known, can provide a better insight into the RNA 3D structural motifs as well as their characteristic functions in cell biology. Results In this work, we proposed a method, RNAMotifComp, that analyzes the instances of well-known structural motif families and establishes a relational graph among them. We also have designed a method to visualize the relational graph where the families are shown as nodes and their similarity information is represented as edges. We validated our discovered correlations of the motif families using RNAMotifContrast. Additionally, we used a basic Naïve Bayes classifier to show the importance of RNAMotifComp. The relational analysis explains the functional analogies of divergent motif families and illustrates the situations where the motifs of disparate families are predicted to be of the same family. Availability and implementation Source code publicly available at https://github.com/ucfcbb/RNAMotifFamilySimilarity.