.png){kind=icon}
Return
Machine learning model for sequence-driven DNA G-quadruplex formation
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-017-14017-4.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
STABILITY
LOOP
MOTIFS
LONG
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.9W
Citations:
83.5W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveHow long is too long? Effects of loop size on G-quadruplex stability.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveGenome-wide transcriptional profiling of the Bacillus subtilis cold-shock responseMicrobiology
OAAI
MicrobiologyIF0
OAAIShareSaveShareSaveShareSave
View more