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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
Transcription Factors02:16

Transcription Factors

75.8K
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...
75.8K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

9.8K
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...
9.8K
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
General Transcription Factors01:30

General Transcription Factors

5.2K
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...
5.2K
Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

11.0K
Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These...
11.0K

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相关实验视频

Updated: Jun 24, 2025

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

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使用CnNet方法预测转录因子结合站点.

M Mohamed Divan Masood, D Manjula, Vijayan Sugumaran

    IEEE/ACM transactions on computational biology and bioinformatics
    |June 7, 2024
    PubMed
    概括

    本研究介绍了CnNet,这是一种深度学习方法,通过分析DNA序列特征来预测转录因子结合位. CnNet增强了基因调节元件的发现,改善了疾病研究.

    科学领域:

    • 基因组学就是基因组学.
    • 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:

    • 控制基因表达对于理解生物过程和疾病至关重要.
    • 识别调节基因表达的因素,如转录因子 (TFs),至关重要但具有挑战性.
    • 目前用于发现TF绑定站点和监管元素的方法需要新的计算方法.

    研究的目的:

    • 开发和评估一种基于深度学习的计算方法,用于预测转录因子结合.
    • 识别DNA基因序列的序列特异性,以改善TF结合部位的预测.
    • 与现有方法相比,提高预测TF约束性得分的准确性.

    主要方法:

    • 使用深度学习技术,特别是卷积神经网络 (CNN).
    • 采用多个表达式动机引发动机 (MEME) 技术来发现序列动机.
    • 开发了一种名为CnNet的新方法,将MEME和CNN结合起来,用于TF绑定站点预测.

    主要成果:

    • CnNet方法成功地从实验数据中识别出序列特异性.
    • 使用MEME技术发现了TF结合部位的标志性图案.
    • CnNet的CNN组件以高准确度计算了TF绑定的概率得分.

    更多相关视频

    PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
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    PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

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    High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
    06:38

    High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

    Published on: February 7, 2019

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    相关实验视频

    Last Updated: Jun 24, 2025

    Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
    11:34

    Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

    Published on: August 9, 2019

    6.6K
    PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
    12:24

    PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

    Published on: July 2, 2010

    53.4K
    High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
    06:38

    High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

    Published on: February 7, 2019

    8.7K

    结论:

    • 拟议的CnNet方法提供了一个可扩展,灵活和统一的计算策略,用于预测TF绑定.
    • 与现有的方法相比,CnNet在预测TF约束性得分方面显示出明显提高的准确性.
    • 这项研究促进了对基因调控机制和疾病相关因素的理解.