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

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.4K
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...
6.4K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

910
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...
910
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.8K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
6.8K
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

7.4K
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...
7.4K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

3.0K
3.0K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

3.0K
3.0K

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

Gelatin-polyelectrolyte polysaccharide complexes for encapsulation of natural pigments: formation mechanisms and functionality.

Critical reviews in food science and nutrition·2026
Same author

LncRNA MIAT Protects Against Sevoflurane-Induced Cognitive Dysfunction in Neonatal Rats via the miR-15b-5p/Ccnd1 Axis.

Synapse (New York, N.Y.)·2026
Same author

[Impact and Pathways of Environmental Risks on Urban Resilience in Xi'an].

Huan jing ke xue= Huanjing kexue·2026
Same author

Colloid milling and microfluidization assisted by pH regulation improve the solubility and functional properties of yeast proteins.

Food chemistry·2026
Same author

Germline regulation of tumor evolutionary dynamics shapes multiple myeloma progression.

bioRxiv : the preprint server for biology·2026
Same author

Convergent Evolution in Tumor Genomes Targets Functional Domains.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jul 4, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

727

DeepCORE:一种可解释的多视图深度神经网络模型,用于检测合作性监管元素.

Pramod Bharadwaj Chandrashekar1,2, Hai Chen3,4, Matthew Lee3

  • 1Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA.

Computational and structural biotechnology journal
|January 31, 2024
PubMed
概括

我们开发了DeepCORE,这是一种新的深度学习方法,用于识别控制基因转录的合作性调节元件 (CORE). 通过分析遗传和表观遗传数据,DeepCORE准确地预测基因表达,并发现新的调节元素.

关键词:
合作性监管要素 合作性监管元素深度学习是一种深度学习.表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.基因调节 基因调节

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jul 4, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

727
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

  • 基因转录对于细胞功能,特征和疾病至关重要,由相互作用元素的复杂网络调节.
  • 了解这些合作性调节元件 (CORE) 是解读基因表达控制的关键.

研究的目的:

  • 通过整合遗传,表观遗传和转录数据来开发一种新的深度学习方法来识别CORE.
  • 准确预测基因表达并发现涉及转录的新型调节元素.

主要方法:

  • 一个基于多视图注意力的深度神经网络,DeepCORE,被开发用于模拟不同生物数据类型之间的关系.
  • DeepCORE使用解释器来提取注意力值,将它们映射到监管区域,并根据相关的注意力模式推断CORE.

主要成果:

  • DeepCORE准确地预测了各种组织和细胞系的转录组,超过了现有的最先进的算法.
  • 确定的CORE被显著丰富了已知的基因调节元素,如促进剂和增强剂.
  • 由DeepCORE发现的新型调节元素表现出与基因组修饰模式一致的表观遗传特征.

结论:

  • DeepCORE提供了一种强大的新方法来剖析复杂的基因调节网络.
  • 该方法成功地识别了已知的和新的监管元素,进步了我们对转录控制的理解.