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Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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相关实验视频

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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促进:基于图形嵌入和集体学习的促进者预测,用于真核生物序列.

Yan Wang1,2, Shiwen Tai1, Shuangquan Zhang3

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.

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概括

通过使用一种基于图形的新方法,PromGER通过整合本地和全球序列特征,准确地预测真核细胞促进子序列. 这种方法提高了对基因调节和转录活动的理解.

关键词:
组合学习组合学习图形嵌入 图形嵌入.可以解释的解释性.推广者预测预测序列分析分析的序列分析.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.
  • 计算生物学 计算生物学

背景情况:

  • 促使者是控制基因转录的关键DNA调节区域.
  • 准确的促进子预测对于理解基因功能和转录活性至关重要.
  • 现有的机器和深度学习模型在捕获复杂的促进器序列关系和生物信息方面存在局限性.

研究的目的:

  • 开发一种新的预测模型,PromGER,用于真核细胞促进子序列.
  • 通过提取更深层次的生物信息和考虑复杂的序列关系,克服当前模型的局限性.
  • 为了提高推动者预测的准确性和可解释性.

主要方法:

  • PromGER采用四种特征编码方法,从促进器序列中提取本地信息.
  • 序列以图形形式表示以捕捉潜在的关系,使用图形嵌入方法用于全球特征提取.
  • 一个基于树的集体学习框架整合了本地和全球特征进行预测.

主要成果:

  • 与七种现有方法相比,PromGER表现出优越的性能.
  • 在特异性 (13%),准确性 (10%),马修相关系数 (16%),精度 (4%),F1得分 (6%) 和AUC (9%) 中观察到显著改善.
  • 使用t分布式随机邻居嵌入 (t-SNE) 和SHAPley添加式扩展 (SHAP) 分析证实了模型的解释性.

结论:

  • PromGER提供了一种强大且可解释的方法来预测真核细胞促进子序列.
  • 在基于图形的框架中整合本地和全球特征可以提高预测准确性.
  • 这项研究推进了基因调节研究的生物信息学工具.