  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.
