School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, Zhejiang, China.
Brief Funct Genomics. 2024 May 15;23(3):265-275. doi: 10.1093/bfgp/elad024.
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.
四链体（G4）是一种非经典的脱氧核糖核酸结构，广泛分布于基因组中，参与多种生物学过程。在体内，高通量测序表明，G4 以细胞类型特异性的方式在功能区域中显著富集。因此，基于计算方法预测 G4 是必要的，而不是耗时和费力的实验方法。最近，开发了 G4 CUT&Tag 来生成比 ChIP-seq 分辨率更高的测序数据，为模型构建提供了更准确的训练样本。在本文中，我们提出了一种基于 G4 CUT&Tag 测序数据的新数据集构建方法和一种基于机器学习提升方法的 XGBoost 预测模型。结果表明，我们的模型在细胞内和细胞间都表现良好。此外，序列分析表明，G4 结构的形成受侧翼序列的影响很大，G4 侧翼序列的 GC 含量高于非 G4。此外，我们还在高分辨率数据集中识别了 G4 基序，其中包括几个已知转录因子（TF）的基序，如 SP2 和 BPC。这些 TF 可能直接或间接影响 G4 结构的形成。
