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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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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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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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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...
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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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相关实验视频

Updated: Jun 12, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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咖啡:对基因调节网络的共识单细胞类型特定推断.

Musaddiq K Lodi1, Anna Chernikov2, Preetam Ghosh3

  • 1Integrative Life Sciences, Virginia Commonwealth University, 1000 W Cary St, Richmond, VA 23284, United States.

Briefings in bioinformatics
|September 23, 2024
PubMed
概括

咖啡是一种共识算法,通过整合多种方法,改善了对单细胞RNA测序 (scRNA-seq) 数据的基因调控网络 (GRN) 推断. 这种方法可以提高各种数据集的准确性,为生物研究提供灵活的策略.

关键词:
基因监管网络 基因监管网络单细胞生物学 单细胞生物学转录机制的转录机制.人群的智慧 - - 人群的智慧.

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科学领域:

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

背景情况:

  • 基因调节网络 (GRNs) 对于理解生物过程至关重要.
  • 从scRNA-seq数据计算推断GRN是一个关键的挑战.
  • 整合多种GRN推断方法可以提高性能.

研究的目的:

  • 从scRNA-seq数据开发一个用于细胞类型特定GRN推断的共识算法.
  • 评估共识算法的性能与基线方法相比.
  • 为了证明共识方法的灵活性和适用性.

主要方法:

  • 开发了一种基于Borda投票的共识算法COFFEE (对gEnE监管网络的共识),这是一个基于Borda投票的共识算法.
  • 综合了来自 10 种已建立的 GRN 推断方法的信息.
  • 在合成,策划和实验性scRNA-seq数据集上的基准咖啡.

主要成果:

  • 与个人基线GRN推断方法相比,COFFEE表现得更好.
  • 一个修改后的COFFEE版本提高了较新的细胞类型特定GRN推断方法的性能.
  • 共识方法在单细胞层面的GRN推断中被证明是有价值的.

结论:

  • 基于共识的方法,如COFFEE,对于scRNA-seq数据中的GRN推断是有效的.
  • 咖啡提供了一个灵活的框架,可以适应各种GRN推断算法.
  • 这项研究强调了在计算生物学中集体策略的持续重要性.