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相关概念视频

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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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.
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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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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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可扩展的细胞特异性共同表达网络用于使用NeighbourNet进行细粒度的监管模式发现.

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  • 1The University of Melbourne, The Australian National University.

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邻居网络 (NNet) 从单细胞RNA测序数据中构建细胞特异性基因协同表达网络. 这种方法捕捉了单个细胞之间的动态监管变化,改善了对大型数据集的网络推理.

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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:

  • 基因网络对于理解基因表达调节至关重要.
  • 单细胞RNA测序 (scRNA-seq) 允许以细胞分辨率进行网络推断.
  • 现有的方法通常假设静态的调节程序,缺少动态的细胞变异.

研究的目的:

  • 介绍NeighbourNet (NNet),一种用于构建细胞特异性同表达网络的新方法.
  • 解决现有方法在scRNA-seq数据中捕获动态调节变化的局限性.
  • 为分析大规模单细胞数据集提供可扩展的框架.

主要方法:

  • 邻居网络 (NNet) 使用主要组件分析将基因表达嵌入低维空间.
  • 在k-最近邻居 (KNN) 内的局部回归量化了细胞特异性共表达.
  • NNet支持可扩展的下游分析,包括元网络聚合和先前知识集成.

主要成果:

  • NNet提高了计算效率,并稳定了scRNA-seq数据的同表达估计.
  • 该方法有效地减轻了KNN回归中的数据噪声,稀疏性和小样本大小的挑战.
  • 案例研究证明了NNet在转录因子活动预测,血液形成和瘤微环境分析方面的实用性.

结论:

  • NNet提供了一个新的框架来探索基因表达的细胞变异.
  • 该R套件与现有的单细胞分析工作流程无集成.
  • 从scRNA-seq数据中,NNet可以对细胞特异性的调控程序进行强有力的推断.