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相关概念视频

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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 dimers that...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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 dimers that...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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 addition of a...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

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Transfecting and Nucleofecting Human Induced Pluripotent Stem Cells
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构建基因调节网络使用查特吉的等级与单细胞转录数据的相关性.

Shreyan Gupta1, Anamitra Chaudhuri2, Vishnuvasan Raghuraman3

  • 1Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA.

bioRxiv : the preprint server for biology
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种新方法,从单细胞RNA测序 (scRNA-seq) 数据中发现基因调控网络 (GRNs). 这种方法在计算上是高效的,并准确地识别了基因相互作用,优于现有的方法.

关键词:
查特吉的相关性是基因监管网络是基因监管网络.有针对性的监管.一个单细胞RNA测序.

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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.
  • 系统生物学 系统生物学

背景情况:

  • 了解基因调节网络 (GRNs) 对细胞功能至关重要.
  • 现有的从单细胞RNA测序 (scRNA-seq) 数据中推断GRN的方法由于强大的假设或高的计算成本而面临限制.
  • 需要用于GRN发现的透明,可扩展和高效的方法.

研究的目的:

  • 引入一种新的多重测试框架,用于从scRNA-seq数据中推断GRNs.
  • 克服现有的GRN推理方法的局限性.
  • 为复杂的机器学习模型提供计算效率高,可扩展的替代方案.

主要方法:

  • 使用Chatterjee的等级相关系数,这是一个非参数的依赖度,用于GRN推理.
  • 开发了一个数据驱动的算法来估计强大的测试截止值,解决scRNA-seq.中的非独立观察.
  • 通过利用查特吉相关性不对称的性质,提出了针对性调节的新测试.

主要成果:

  • 与最先进的方法相比,提出的方法在恢复真正的监管联系方面表现出卓越的表现.
  • 成功应用于模拟和真实scRNA-seq数据集.
  • 能够构建具有生物意义和定向信息的GRNs.

结论:

  • 新的框架为GRN推断提供了一种透明,可扩展和计算效率高的方法.
  • 该方法有效地应对scRNA-seq数据的特定挑战,例如非独立的观测.
  • 为剖析复杂的GRNs提供了强大的工具,并促进了细胞功能的理解.