DeepCBA:基于DNA序列和染色体相互作用的玉米基因表达预测的深度学习框架
Zhenye Wang1, Yong Peng2, Jie Li1
1National 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.
Plant communications
|June 11, 2024
概括
DeepCBA是一种新的深度学习模型,通过整合色素相互作用,准确地预测玉米基因表达. 这种工具有助于识别监管要素并推进精密育种.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 植物科学 植物科学
背景情况:
- 染色体相互作用通过将调节元件连接到目标基因来影响基因表达和特征.
- 当前的基因表达预测方法往往忽视了染色体相互作用,限制了准确性和调节元素的发现.
- 玉米 (Zea mays) 的基因调节是复杂的,需要先进的计算工具来准确地预测表达.
研究的目的:
- 开发一个高度准确的深度学习模型,DeepCBA,通过结合色素相互作用数据来预测玉米中的基因表达.
- 评估DeepCBA的性能与现有的基因表达分类和价值预测方法相比.
- 确定影响基因表达的新型调节动机和元素,并验证该模型在基因功能探索和繁殖中的实用性.
主要方法:
- 开发DeepCBA,一个深度学习模型,利用玉米染色素相互作用数据.
- 使用皮尔森相关系数 (PCC) 进行DeepCBA与传统方法的比较分析.
- 在特定的基因组区域中丰富的重要基因的识别和表征,并表现出组织特异性.
主要成果:
- 在基因表达预测方面,DeepCBA取得了高准确度,在考虑近位和远位相互作用时,PCC达到0.929.
- 该模型显著优于传统方法,在不同类型的相互作用中显示PCC的大幅增加.
- 深度CBA确定了生物相关的动机,通过对玉米基因 (ZmRap2.7,ZmTb1) 的实验分析和促进器编辑 (ZmCLE7,ZmVTE4) 进行验证.
结论:
- DeepCBA提供了一种强大而准确的方法,通过利用染色体相互作用数据来预测玉米中的基因表达.
- 该模型促进了功能调节元件的发现,并提供了对基因调节的见解.
- DeepCBA展示了精确的基因表达设计和智能育种策略中的应用的巨大潜力.
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