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ABSTRACT: 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
SUBMITTER: Garant JM
PROVIDER: S-EPMC5870565 | biostudies-literature | 2017 Nov
REPOSITORIES: biostudies-literature