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相关概念视频

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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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Prokaryotic Transcriptional Activators and Repressors01:58

Prokaryotic Transcriptional Activators and Repressors

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The organization of prokaryotic genes in their genome is notably different from that of eukaryotes. Prokaryotic genes are organized, such that the genes for proteins involved in the same biochemical process or function are located together in groups. This group of genes, along with their regulatory elements, are collectively known as an operon. The functional genes in an operon are transcribed together to give a single strand of mRNA known as polycistronic mRNA.
Transcription of prokaryotic...
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Operons02:09

Operons

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Prokaryotes can control gene expression through operons—DNA sequences consisting of regulatory elements and clustered, functionally related protein-coding genes. Operons use a single promoter sequence to initiate transcription of a gene cluster (i.e., a group of structural genes) into a single mRNA molecule. The terminator sequence ends transcription. An operator sequence, located between the promoter and structural genes, prohibits the operon’s transcriptional activity if bound by...
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Co-activators and Co-repressors02:04

Co-activators and Co-repressors

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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相关实验视频

Updated: Jul 5, 2025

DNA-affinity-purified Chip DAP-chip Method to Determine Gene Targets for Bacterial Two component Regulatory Systems
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推断的调节子与大肠杆菌中的调节器结合序列一致.

Sizhe Qiu1, Xinlong Wan1, Yueshan Liang1

  • 1Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America.

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概括
此摘要是机器生成的。

机器学习模型证实,从RNA-seq数据推断出的细菌规律在促进体DNA序列中具有强大的生物化学基础. 促销器序列特征,包括动机和DNA形状,成功预测了调节活动,验证了用于发现转录调节网络的自上而下的推理方法.

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 大肠杆菌中的转录调节网络 (TRN) 涉及调节剂和DNA序列之间的复杂相互作用.
  • 调节因子通过实验性结合部位测量或从基因表达数据推断来确定.
  • RNA-seq数据的独立组件分析 (ICA) 是推断细菌规律的强大工具.

研究的目的:

  • 用促进子序列特征研究ICA推断的调节子结构的生物化学基础.
  • 开发和验证机器学习模型,以基于促进器序列来预测大肠杆菌规律结构.
  • 评估促进子序列特征在多大程度上解释ICA推断的规律组织.

主要方法:

  • 机器学习模型的开发,以预测大肠杆菌的规律结构.
  • 利用促进子序列特征,包括调节器图案,DNA形状和用于多分子结合的扩展图案.
  • 使用AUROC (接收器操作特征曲线下的面积) 进行交叉验证,用于模型性能评估.
  • 对最初模型未能识别出新的序列特征的 regulon 的分析.

主要成果:

  • 机器学习模型成功预测了85%的ICA推断的大肠杆菌规律的规律结构 (AUROC >= 0.8).
  • 仅仅促进者动机就预测了40%的监管活动.
  • 额外的特征,如DNA形状和扩展的图案,改善了预测剩余的60%的规律.
  • 对模型故障的调查揭示了新的监管器特定特征,提高了准确性.

结论:

  • 根据ICA推断的规则的结构在很大程度上可以通过促进区域的监管绑定站点的强度来解释.
  • 促进子序列特征为自上而下的规律推理提供了生化基础.
  • 这项研究加强了ICA和机器学习在发现细菌转录性调节网络方面的实用性.