Machine learning model for sequence-driven DNA G-quadruplex formation

Abstract We describe a sequence-based computational model to predict DNA G-quadruplex (G4) formation. The model was developed using large-scale machine learning from an extensive experimental G4-formation dataset, recently obtained for the human genome via G4-seq methodology. Our model differentiate...

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Autores principales:	Aleksandr B. Sahakyan, Vicki S. Chambers, Giovanni Marsico, Tobias Santner, Marco Di Antonio, Shankar Balasubramanian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/289341f558e943b6bbdbc39c89dcf616
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spelling	oai:doaj.org-article:289341f558e943b6bbdbc39c89dcf6162021-12-02T15:05:14ZMachine learning model for sequence-driven DNA G-quadruplex formation10.1038/s41598-017-14017-42045-2322https://doaj.org/article/289341f558e943b6bbdbc39c89dcf6162017-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-14017-4https://doaj.org/toc/2045-2322Abstract We describe a sequence-based computational model to predict DNA G-quadruplex (G4) formation. The model was developed using large-scale machine learning from an extensive experimental G4-formation dataset, recently obtained for the human genome via G4-seq methodology. Our model differentiates many widely accepted putative quadruplex sequences that do not actually form stable genomic G4 structures, correctly assessing the G4 folding potential of over 700,000 such sequences in the human genome. Moreover, our approach reveals the relative importance of sequence-based features coming from both within the G4 motifs and their flanking regions. The developed model can be applied to any DNA sequence or genome to characterise sequence-driven intramolecular G4 formation propensities.Aleksandr B. SahakyanVicki S. ChambersGiovanni MarsicoTobias SantnerMarco Di AntonioShankar BalasubramanianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aleksandr B. Sahakyan Vicki S. Chambers Giovanni Marsico Tobias Santner Marco Di Antonio Shankar Balasubramanian Machine learning model for sequence-driven DNA G-quadruplex formation
description	Abstract We describe a sequence-based computational model to predict DNA G-quadruplex (G4) formation. The model was developed using large-scale machine learning from an extensive experimental G4-formation dataset, recently obtained for the human genome via G4-seq methodology. Our model differentiates many widely accepted putative quadruplex sequences that do not actually form stable genomic G4 structures, correctly assessing the G4 folding potential of over 700,000 such sequences in the human genome. Moreover, our approach reveals the relative importance of sequence-based features coming from both within the G4 motifs and their flanking regions. The developed model can be applied to any DNA sequence or genome to characterise sequence-driven intramolecular G4 formation propensities.
format	article
author	Aleksandr B. Sahakyan Vicki S. Chambers Giovanni Marsico Tobias Santner Marco Di Antonio Shankar Balasubramanian
author_facet	Aleksandr B. Sahakyan Vicki S. Chambers Giovanni Marsico Tobias Santner Marco Di Antonio Shankar Balasubramanian
author_sort	Aleksandr B. Sahakyan
title	Machine learning model for sequence-driven DNA G-quadruplex formation
title_short	Machine learning model for sequence-driven DNA G-quadruplex formation
title_full	Machine learning model for sequence-driven DNA G-quadruplex formation
title_fullStr	Machine learning model for sequence-driven DNA G-quadruplex formation
title_full_unstemmed	Machine learning model for sequence-driven DNA G-quadruplex formation
title_sort	machine learning model for sequence-driven dna g-quadruplex formation
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/289341f558e943b6bbdbc39c89dcf616
work_keys_str_mv	AT aleksandrbsahakyan machinelearningmodelforsequencedrivendnagquadruplexformation AT vickischambers machinelearningmodelforsequencedrivendnagquadruplexformation AT giovannimarsico machinelearningmodelforsequencedrivendnagquadruplexformation AT tobiassantner machinelearningmodelforsequencedrivendnagquadruplexformation AT marcodiantonio machinelearningmodelforsequencedrivendnagquadruplexformation AT shankarbalasubramanian machinelearningmodelforsequencedrivendnagquadruplexformation
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Machine learning model for sequence-driven DNA G-quadruplex formation

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