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相关概念视频

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

11.6K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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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
PubMed
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.

关键词:
关联监管要素 关联监管要素深度学习是一种深度学习.可以解释性 解释性预测系统的预测系统.

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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.