Dbert2_LR:一种基于深度学习的模型,用于预测作物中的cis-regulatory元素
Huan Liu1, Faxu Guo2, Longyu Huang3
1Institute of Agricultural Information, Chinese Academy of Agricultural Sciences / Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China; National Nanfan Research Institute, Chinese Academy of Agriculture Science (CAAS), Sanya 572024, China; National Agriculture Science Data Center, Beijing 100081, China.
Genomics
|January 16, 2026
概括
我们开发了Dbert2_LR,这是一种深度学习工具,用于在复杂的植物基因组中识别 cis 调节元素 (CREs). 这有助于理解改善作物的基因表达.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 控制基因表达的Cis-regulatory元素 (CREs) 控制基因表达,对于农学特征至关重要.
- 在像棉花这样的大型重复性作物基因组中识别CREs是很困难的.
研究的目的:
- 开发一个高精度的深度学习框架,用于在作物基因组中识别CREs.
- 改进复杂植物基因组的功能注释.
主要方法:
- 开发了Dbert2_LR,这是一个混合深度学习模型,将DNABERT-2与RNN和LSTM网络集成在一起.
- 将模型应用于Arabidopsis thaliana和高地棉花,用于CRE分类.
- 为了模型的可解释性,进行了化和突变发生 (ISM).
主要成果:
- 在分类促进剂,增强剂和非监管序列方面,Dbert2_LR实现了高准确度.
- 超越了基准模型的表现,宏观平均F1得分为0.890 () 和0.637 (棉花).
- ISM分析证实了生物解释性,将预测与已知的转录因子结合动机联系起来.
结论:
- Dbert2_LR是一个功能性注释作物基因组的强大工具.
- 该研究促进了基于CRE的分子育种设计,以改善作物特征.
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