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相关概念视频

Transcription Factors02:16

Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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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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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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General Transcription Factors01:30

General Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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相关实验视频

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DeepEPI:基于CNN变压器的模型,通过预测增强剂-促进剂相互作用来提取TF相互作用.

Seyedeh Fatemeh Tabatabaei1, Saeedeh Akbari Roknabadi2, Somayyeh Koohi1

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, 11155-9517, Iran.

Bioinformatics advances
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DeepEPI是一个新的深度学习框架,通过分析基因组序列,准确地预测增强器-促进器相互作用 (EPI). 该工具增强了基因表达研究和疾病机制研究,提高了效率和可解释性.

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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
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科学领域:

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

背景情况:

  • 增强剂-促进剂相互作用 (EPI) 对基因调节至关重要.
  • 了解EPI是解读基因表达和疾病机制的关键.
  • 现有的EPI预测计算方法在准确性和可解释性方面存在局限性.

研究的目的:

  • 引入DeepEPI,这是一个用于从基因组序列直接预测EPI的深度学习框架.
  • 将DeepEPI的性能与现有模型进行评估,并评估不同的编码方法.
  • 通过分析转录因子 (TF) 相互作用来提高EPI预测的解释性.

主要方法:

  • DeepEPI将卷积神经网络 (CNN) 与变压器块集成在一起.
  • 该框架采用OneHot编码和多头注意力机制的嵌入层.
  • 还开发并评估了基于DNA2Vec的DeepEPI版本.

主要成果:

  • 在6个细胞系中,DeepEPI的表现始终优于现有的模型.
  • 与以前的方法相比,OneHot编码实现了AUPR的4%增加和AUROC的1.9%.
  • DeepEPI成功地提取了生物相关的TF-TF相互作用,帮助实验验证.

结论:

  • DeepEPI为EPI预测提供了一种强大且可解释的深度学习方法.
  • 该框架推动了对基因调节和疾病机制的研究.
  • DeepEPI为表观基因组研究中的实验验证提供了宝贵的见解.