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相关概念视频

Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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相关实验视频

Updated: Jun 30, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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scGREAT:基于转换器的深度语言模型,用于从单细胞转录组学推断基因调节网络.

Yuchen Wang1, Xingjian Chen1,2, Zetian Zheng1

  • 1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.

iScience
|March 21, 2024
PubMed
概括

我们开发了scGREAT,这是一个新的框架,用于从单细胞转录组学推断基因调节网络 (GRNs). 这种方法使用基因嵌入和变压器来克服现有方法的计算限制.

关键词:
生物信息学是一种生物信息学.计算生物信息学是指计算机生物信息学.人类遗传学 人类遗传学

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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科学领域:

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

背景情况:

  • 基因调节网络 (GRNs) 对于理解细胞功能至关重要.
  • 单细胞测序使GRN在颗粒级别的推断成为可能.
  • 现有的方法面临计算成本和简单的假设.

研究的目的:

  • 引入scGREAT,这是一个用于从单细胞转录组学推断GRN的新框架.
  • 解决当前GRN推断工具的计算和基于假设的限制.

主要方法:

  • scGREAT从scRNA-seq数据中构建基因表达和生物文本字典.
  • 它使用基于变压器的引擎,通过优化的嵌入空间来学习基因对表示.
  • 该框架整合了基因表达和文本信息,以便进行可靠的GRN推断.

主要成果:

  • 与基准数据集上的当代方法相比,scGREAT 显示出更高的性能.
  • 由scGREAT生成的基因表征为基因调节提供了重要的见解.
  • 使用空间转录学的外部验证证实了scGREAT的机械注释.

结论:

  • scGREAT为推断基因调节网络提供了一个有效和准确的框架.
  • 该方法提供了宝贵的基因调节见解,并确定了新的TF-目标相互作用.
  • scGREAT在单细胞GRN推断领域取得了进展.