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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2017-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-14017-4 |
| _version_ | 1818835127407476736 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T02:45:46Z |
| format | Article |
| id | doaj.art-289341f558e943b6bbdbc39c89dcf616 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T02:45:46Z |
| publishDate | 2017-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-289341f558e943b6bbdbc39c89dcf6162022-12-21T20:38:54ZengNature PortfolioScientific Reports2045-23222017-11-017111110.1038/s41598-017-14017-4Machine learning model for sequence-driven DNA G-quadruplex formationAleksandr B. Sahakyan0Vicki S. Chambers1Giovanni Marsico2Tobias Santner3Marco Di Antonio4Shankar Balasubramanian5Department of Chemistry, University of Cambridge, Lensfield RoadDepartment of Chemistry, University of Cambridge, Lensfield RoadDepartment of Chemistry, University of Cambridge, Lensfield RoadDepartment of Chemistry, University of Cambridge, Lensfield RoadDepartment of Chemistry, University of Cambridge, Lensfield RoadDepartment of Chemistry, University of Cambridge, Lensfield RoadAbstract 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.https://doi.org/10.1038/s41598-017-14017-4 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | Machine learning model for sequence-driven DNA G-quadruplex formation |
| title_sort | machine learning model for sequence driven dna g quadruplex formation |
| url | https://doi.org/10.1038/s41598-017-14017-4 |
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