RNAMotifContrast: a method to discover and visualize RNA structural motif subfamilies

Abstract

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.

RNA-NRD: a non-redundant RNA structural dataset for benchmarking and functional analysis

“…Finding the best possible representative for a redundant set of macromolecule structures will not only reduce this unnecessary data bias and noise, but it will also make further analysis of the dataset computationally more efficient. RNA 3D structure prediction ( 11 , 12 ), computational modeling and analysis ( 13 ), structural motif family and subfamily identification ( 14 ) are some of the major fields in which the redundant structures can affect significantly. Nevertheless, the work done so far for organizing and classifying RNA chains is mere compared to protein chains.…”

A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures

“…The majority of the tools focus on producing a structure-based RNA sequence alignment [42][43][44][45][46][47][48][49] and therefore usually require single RNA chains as input [43,44,47]. Another large group of tools is focused on local tertiary motifs superposition and search [50][51][52][53]. Hence, these tools rely on some of the structural features of the local motifs, e.g., on sequence conservation, annotation of canonical and non-canonical interactions, and backbone topology.…”

RNAglib: A Python Package for RNA 2.5D Graphs

“…To capture the tertiary structure of RNA in a computationally feasible manner, a growing number of algorithms make use of 2.5D graph networks [19,15,9,20,18,3]. These networks represent RNA molecules as topological graphs, whose nodes are nucleotides and whose edge types are structural categories of interactions.…”