VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs

TL;DR

VeRNAl introduces a novel graph learning approach to identify flexible RNA 3D motifs, enabling the discovery of both known and new motifs by modeling structural variability efficiently.

Contribution

The paper presents a new graph representation learning and clustering framework for RNA motif discovery, relaxing previous constraints and allowing customizable flexibility and size.

Findings

Successfully retrieves known RNA motifs

Proposes novel RNA motifs

Flexible and customizable motif detection

Abstract

RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods, and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs.