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相关概念视频

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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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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

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The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
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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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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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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相关实验视频

Updated: Sep 8, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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脚本:预测基于预训练的图形注意网络的单细胞远程CIS调节.

Yu Zhang1,2,3, Baole Wen4, Yifeng Jiao2

  • 1Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, 200433, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|August 20, 2025
PubMed
概括

使用图形因果注意网络,SCRIPT准确地预测了单细胞的cis-regulatory关系 (CRR). 这种方法增强了对基因调节和疾病机制的理解,优于现有的工具.

关键词:
在 cis-regulation 中进行调节.图形神经网络的神经网络没有编码的变体.列车前列车的前列车是什么一个单细胞的单细胞.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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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
  • 分子生物学分子生物学

背景情况:

  • 单细胞Cis调节关系 (CRRs) 对于理解基因调节和疾病机制至关重要.
  • 目前的计算方法在预测单细胞CRR方面缺乏准确性,原因是生物原理和大规模单细胞数据的整合不足.

研究的目的:

  • 开发一种新的计算方法,SCRIPT,从转录组和染色体可访问性数据准确推断单细胞CRR.
  • 改进对长期资产负债权的预测,并促进疾病引起变异的识别.

主要方法:

  • SCRIPT使用图形因果注意网络,结合经验CRR证据.
  • 通过对亚特拉斯尺度单细胞染色体可访问性数据的预训练,增强了代表性学习.
  • 验证涉及细胞类型特定的染色质接触和CRISPR扰动数据.

主要成果:

  • 斯克里普特的平均AUC为0.89,明显超过了最先进的方法 (AUC:0.7).
  • 斯克里普特在预测长期CRA (>100 Kb) 方面表现出超过两倍的改进.
  • 对阿尔茨海默病和精神分裂症的应用优先考虑引起疾病的变体,并阐明它们对细胞类型特定的功能影响.

结论:

  • SCRIPT提供了一个强大的框架来推断单细胞RCRs,推进基因诊断和目标发现.
  • 该方法揭示了现有的计算方法错过的分子遗传机制.
  • SCRIPT提供了一份路线图,以了解疾病中非编码变异的功能影响.