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相关概念视频

Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
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...
8.3K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

9.9K
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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相关实验视频

Updated: Jun 25, 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

Published on: March 1, 2024

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从基于图形嵌入的单细胞转录组学推断基因调控网络.

Yanglan Gan1, Jiacheng Yu1, Guangwei Xu1

  • 1School of Computer Science and Technology, Donghua University, Shanghai 201620, China.

Bioinformatics (Oxford, England)
|May 29, 2024
PubMed
概括

从单细胞RNA测序 (scRNA-seq) 数据中推断基因调控网络 (GRNs) 是一个挑战. 我们的IGEGRNS深度学习框架通过考虑全球基因关系和远程调节连接来改善GRN推断.

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) 对于理解生物过程至关重要.
  • 从单细胞RNA测序 (scRNA-seq) 数据中推断GRNs存在挑战,原因是基因调控的动态和复杂性质.
  • 现有的方法往往忽略了全球结构和远程监管关系.

研究的目的:

  • 开发一个新的监督深度学习框架,IGEGRNS,用于从scRNA-seq数据中准确推断GRN.
  • 通过整合全球网络结构和远程监管信息来解决现有方法的局限性.
  • 通过改进GRN重建,增强对复杂生物过程的理解.

主要方法:

  • 开发了IGEGRNS,这是一个使用图形嵌入的监督深度学习框架.
  • 采用 GraphSAGE 来捕获上下文基因信息,通过将特征和邻近结构聚合到低维嵌入中.
  • 利用Top-k聚合来识别有影响力的节点和卷积神经网络 (CNN) 来预测监管关系.

主要成果:

  • 与其他九种监督和无监督方法相比,IGEGRNS框架显示出更高的性能.
  • 在6个时间序列scRNA-seq数据集中推断基因调节网络的准确性提高.

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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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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

Published on: March 1, 2024

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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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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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Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

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  • 成功捕获了全球网络结构和远程监管关系,改进了现有的方法.

    • 结论:

    • IGEGRNS提供了一种强大而有效的方法,可以从scRNA-seq数据中推断基因调节网络.
    • 该框架能够整合全球网络拓和远程交互提供了显著的优势.
    • 这一进步有助于更深入地了解基因调节和复杂的生物系统.