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

Klimentova, Eva and Polacek, Jakub and Simecek, Petr and Alexiou, Panagiotis (2020) PENGUINN: Precise Exploration of Nuclear G-Quadruplexes Using Interpretable Neural Networks. Frontiers in Genetics, 11. ISSN 1664-8021

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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.

Item Type: Article
Subjects: STM Archives > Medical Science
Depositing User: Unnamed user with email support@stmarchives.com
Date Deposited: 28 Jan 2023 09:04
Last Modified: 01 Jul 2024 11:24
URI: http://science.scholarsacademic.com/id/eprint/168

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