Motif independent identification of potential RNA G-quadruplexes by G4RNA screener

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(a parts per thousand<yen>3)N(1-7) G(a parts per thousand<yen>3)N(1-7) G(a parts per thousand<yen>3)N(1-7) G(a parts per thousand<yen>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. 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.