Motivation: G-quadruplex structures in RNA molecules are known to have regulatory impacts in cells but are difficult to locate in the genome. The minimal requirements for G-quadruplex folding in RNA (G≥3N1-7 G≥3N1-7 G≥3N1-7 G≥3) is being challenged by observations made on specific examples in recent years. The definition of potential G-quadruplex sequences has major repercussions on the observation of the structure since it introduces a bias. The canonical motif only describes a sub-population of the reported G-quadruplexes. To address these issues, we propose an RNA G-quadruplex prediction strategy that does not rely on a motif definition.
Results: We trained an artificial neural network with sequences of experimentally validated G-quadruplexes from the G4RNA database encoded using an abstract definition of their sequence. This artificial neural network, G4NN, evaluates the similarity of a given sequence to known G-quadruplexes and reports it as a score. G4NN has a predictive power comparable to the reported G richness and G/C skewness evaluations that are the current state-of-the-art for the identification of potential RNA G-quadruplexes. We combined these approaches in the G4RNA screener, a program designed to manage and evaluate the sequences to identify potential G-quadruplexes.
Availability and implementation: G4RNA screener is available for download at http://gitlabscottgroup.med.usherbrooke.ca/J-Michel/g4rna_screener.
Contact: jean-michel.garant@usherbrooke.ca or jean-pierre.perreault@usherbrooke.ca or michelle.scott@usherbrooke.ca.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author 2017. Published by Oxford University Press.