Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Tensile strength suppresses the osteogenesis of periodontal ligament cells in inflammatory microenvironments.

Molecular medicine reports·2017
Same author

A PDGFB mutation causes paroxysmal nonkinesigenic dyskinesia with brain calcification.

Movement disorders : official journal of the Movement Disorder Society·2017
Same author

A Molecular Switch Regulating Cell Fate Choice between Muscle Progenitor Cells and Brown Adipocytes.

Developmental cell·2017
Same author

Microarray analysis of differentially expressed genes and their functions in omental visceral adipose tissues of pregnant women with vs. without gestational diabetes mellitus.

Biomedical reports·2017
Same author

Development and validation of a simplified titration method for monitoring volatile fatty acids in anaerobic digestion.

Waste management (New York, N.Y.)·2017
Same author

Association Analysis of Nonsyndromic Congenital Heart Disease and Tag Single Nucleotide Polymorphisms of TBX20 and Genes in the Ras-MAPK Pathway.

Genetic testing and molecular biomarkers·2017

相关实验视频

Updated: Jun 4, 2025

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries
10:10

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries

Published on: March 31, 2019

8.3K

预测人类基因组中CTCF细胞类型的活性结合部位.

Lu Chai1, Jie Gao1, Zihan Li1

  • 1School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, People's Republic of China.

Scientific reports
|December 31, 2024
PubMed
概括

在基因组调节方面,CCCTC-约束因子 (CTCF) 是至关重要的. 机器学习确定了RAD21/SMC3和染色质可访问性作为跨细胞类型中CTCF结合活性的关键预测因素.

关键词:
在CTCF的绑定站点.染色体的可访问性 染色体的可访问性卷积神经网络是一种卷积神经网络.在RAD21中使用RAD21.在SMC3中,SMC3是SMC3.

更多相关视频

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.3K
Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin
10:05

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Published on: January 20, 2016

8.2K

相关实验视频

Last Updated: Jun 4, 2025

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries
10:10

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries

Published on: March 31, 2019

8.3K
Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.3K
Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin
10:05

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Published on: January 20, 2016

8.2K

科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • CCCTC-约束因子 (CTCF) 在基因组组织和调节中起着至关重要的作用.
  • 了解CTCF细胞类型特定的DNA结合对于破译其多样化的生物功能至关重要.
  • 目前关于细胞类型活性CTCF结合位的决定因素的知识仍然有限.

研究的目的:

  • 调查影响CTCF细胞类型特定DNA结合亲和力的关键因素.
  • 使用机器学习开发CTCF绑定活动的预测模型.
  • 提高对CTCF在人类基因组中的调控作用的理解.

主要方法:

  • 在ENCODE项目中,从67个细胞系中收集并策划了CTCF的ChIP-seq数据.
  • 开发了一个独特的细胞类型活性CTCF结合位点 (CBS) 数据集.
  • 训练有素的卷积神经网络 (CNN) 识别与CTCF结合活动相关的模式.

主要成果:

  • 转录因子RAD21/SMC3和染色质可访问性被认为比序列动机或基因组修饰更能预测CTCF结合.
  • 结合这些因素的综合模型实现了精度回忆曲线下的面积 (AUPRC) 值始终高于0.868.8.
  • 这些发现突显了RAD21/SMC3和染色质可访问性对CTCF结合动态的预测能力.

结论:

  • 机器学习框架可以有效地破译CTCF转录因子结合的复杂模式.
  • RAD21/SMC3和染色质可访问性是细胞类型特定CTCF结合的关键决定因素.
  • 这项研究促进了对CTCF的监管功能和基因组组织机制的理解.