Prediction of DNA i-Motifs Via Machine Learning
Prediction of DNA i-Motifs Via Machine Learning
复制标题
通过机器学习预测 DNA i-Motif
DOI：
10.1101/2023.12.11.571121
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yang B
中科院分区：
文献类型：
--
作者：
Yang B
i-Motifs（iM）是在富含胞嘧啶的DNA序列中形成的二级结构，并且参与基因组中的多种功能。虽然推定的iM形成序列广泛分布在人类基因组中，但推定的iM的折叠状态和强度变化很大。许多以前的研究iM集中在评估iM的折叠特性，使用生物物理实验。然而，目前还没有专门的计算工具来预测iM结构的折叠状态和强度。在这里，我们介绍了一个机器学习管道，iM-Seeker，来预测DNA iMs的折叠状态和结构稳定性。该程序iM-Seeker结合了一个平衡随机森林分类器，该分类器在全基因组iMab基于抗体的CUT&Tag测序数据上进行训练，以预测折叠状态，并结合了一个极端梯度提升回归器，以根据文献生物物理数据和我们的内部生物物理实验来估计折叠强度。iM-Seeker预测DNA iM折叠状态的分类准确率为81%，并在测试集上以0.642的决定系数（R2）估计折叠强度。模型解释证实，富含C的序列的核苷酸组成显著影响iM稳定性，与含有胞嘧啶和胸腺嘧啶的序列呈正相关，与鸟嘌呤和腺嘌呤呈负相关。
i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine.