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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F20%3A00118477" target="_blank" >RIV/00216224:14740/20:00118477 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.frontiersin.org/articles/10.3389/fgene.2020.568546/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fgene.2020.568546/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fgene.2020.568546" target="_blank" >10.3389/fgene.2020.568546</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10603 - Genetics and heredity (medical genetics to be 3)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Genetics

ISSN

1664-8021

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

OCT

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

7

Strana od-do

568546

Kód UT WoS článku

000587687500001

EID výsledku v databázi Scopus

2-s2.0-85095854922