PENGUINN: Precise Exploration of Nuclear G-Quadruplexes Using Interpretable Neural Networks
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
PENGUINN: Precise Exploration of Nuclear G-Quadruplexes Using Interpretable Neural Networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10603 - Genetics and heredity (medical genetics to be 3)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Genetics
ISSN
1664-8021
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
OCT
Country of publishing house
CH - SWITZERLAND
Number of pages
7
Pages from-to
568546
UT code for WoS article
000587687500001
EID of the result in the Scopus database
2-s2.0-85095854922