Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data

G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT & Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT & Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.

Automated summary

G-quadruplex (G4) is a non-classical DNA structure that plays an important role in biological processes. Computational methods are necessary for predicting G4s due to their distribution in functional regions in a cell-type-specific manner. In this study, we developed a new prediction model based on machine learning and constructed a dataset using high-resolution sequencing data from G4 CUT & Tag. Our results demonstrate the influence of flanking sequences on G4 formation and the higher GC content of G4 flanking sequences. Furthermore, we identified G4 motifs for known transcription factors in the high-resolution dataset.