PENGUINN: Precise Exploration of Nuclear G-Quadruplexes Using Interpretable Neural Networks

Eva Klimentova; Jakub Polacek; Petr Simecek; Panagiotis Alexiou

doi:10.3389/fgene.2020.568546

PENGUINN: Precise Exploration of Nuclear G-Quadruplexes Using Interpretable Neural Networks

Front Genet. 2020 Oct 27:11:568546. doi: 10.3389/fgene.2020.568546. eCollection 2020.

Authors

Eva Klimentova¹, Jakub Polacek¹, Petr Simecek², Panagiotis Alexiou²

Affiliations

¹ Faculty of Informatics, Masaryk University, Brno, Czechia.
² Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia.

Abstract

G-quadruplexes (G4s) are a class of stable structural nucleic acid secondary structures that are known to play a role in a wide spectrum of genomic functions, such as DNA replication and transcription. The classical understanding of G4 structure points to four variable length guanine strands joined by variable length nucleotide stretches. Experiments using G4 immunoprecipitation and sequencing experiments have produced a high number of highly probable G4 forming genomic sequences. The expense and technical difficulty of experimental techniques highlights the need for computational approaches of G4 identification. Here, we present PENGUINN, a machine learning method based on Convolutional neural networks, that learns the characteristics of G4 sequences and accurately predicts G4s outperforming state-of-the-art methods. We provide both a standalone implementation of the trained model, and a web application that can be used to evaluate sequences for their G4 potential.

Keywords: G quadruplex; bioinformatics and computational biology; deep neural network; genomic; imbalanced data classification; machine learning; web application.