Vernal: a tool for mining fuzzy network motifs in RNA

Author:

Oliver Carlos12ORCID,Mallet Vincent34ORCID,Philippopoulos Pericles5,Hamilton William L12,Waldispühl Jérôme1ORCID

Affiliation:

1. School of Computer Science, McGill University, Montréal, QC H3A 0E9, Canada

2. Montreal Institute for Learning Algorithms (MILA), Montréal, QC H2S 3H1, Canada

3. Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756, Paris, France

4. Mines ParisTech, Paris-Sciences-et-Lettres Research University, Center for Computational Biology, Paris 75272, France

5. Department of Physics, McGill University, Montréal, QC H3A 2T8, Canada

Abstract

Abstract Motivation RNA 3D motifs are recurrent substructures, modeled 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. Results 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. Availability and implementation The source code, data and a webserver are available at vernal.cs.mcgill.ca. We also provide a flexible interface and a user-friendly webserver to browse and download our results. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Fonds de Recherche du Québec Nature et technologies

INCEPTION project

Center for Research and Interdisciplinarity

Discovery grant from the Natural Sciences and Engineering Research Council of Canada

Québec – Nature et technologies

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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