National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
Plant Commun. 2024 Sep 9;5(9):100985. doi: 10.1016/j.xplc.2024.100985. Epub 2024 Jun 10.
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.
染色质相互作用在基因组中创建远端调控元件和靶基因之间的空间接近性，这对基因表达、转录调控和表型特征具有重要影响。迄今为止，已经开发了几种预测基因表达的方法。然而，现有的方法没有考虑到染色质相互作用对靶基因表达的影响，因此可能会降低基因表达预测和重要调控元件挖掘的准确性。在这项研究中，我们基于玉米染色质互作数据开发了一种高度精确的基于深度学习的基因表达预测模型（DeepCBA）。与现有模型相比，DeepCBA 在表达分类和表达值预测方面具有更高的准确性。使用基因启动子近端相互作用、近端-远端相互作用和近端和远端相互作用预测基因表达的平均 Pearson 相关系数（PCC）分别为 0.818、0.625 和 0.929，分别比仅使用基因近端序列的传统方法获得的 PCC 高 0.357、0.16 和 0.469。通过 DeepCBA 鉴定了一些重要的基序；它们在开放染色质区域和表达数量性状位点中富集，并表现出明显的组织特异性。重要的是，对玉米开花相关基因 ZmRap2.7 和分蘖相关基因 ZmTb1 的实验结果表明了 DeepCBA 用于探索影响基因表达的调控元件的可行性。此外，启动子编辑和两个报道基因（ZmCLE7 和 ZmVTE4）的验证表明了 DeepCBA 用于基因表达的精确设计甚至未来智能育种的实用性。DeepCBA 可在 http://www.deepcba.com/ 或 http://124.220.197.196/ 获得。
