School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
Methods. 2024 Sep;229:125-132. doi: 10.1016/j.ymeth.2024.06.012. Epub 2024 Jul 2.
DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.
DNase I 超敏位点（DHSs）是对 DNase I 酶高度敏感的染色质区域。研究 DHSs 对于理解复杂的转录调控机制和定位顺式调控元件（CREs）至关重要。许多研究表明，与疾病相关的基因座通常在 DHSs 区域富集，这凸显了识别 DHSs 的重要性。尽管存在用于 DHSs 鉴定的湿实验，但它们通常劳动强度大。因此，强烈需要为此目的开发计算方法。在这项研究中，我们使用实验数据构建了一个基准数据集。采用了七种特征提取方法来捕获有关人类 DHSs 的信息。应用 F 分数来筛选特征。通过通过五重交叉验证比较各种分类算法的预测性能，提出随机森林来进行最终的模型构建。该模型可以产生 0.859 的总体预测准确性，AUC 值为 0.837。我们希望该模型可以帮助从事 DNase 研究的学者识别这些位点。
