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相关概念视频

RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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相关实验视频

Updated: Jun 21, 2025

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

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scBoolSeq:将scRNA-seq统计和布尔动态联系起来

Gustavo Magaña-López1, Laurence Calzone2,3,4, Andrei Zinovyev5

  • 1Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France.

PLoS computational biology
|July 8, 2024
PubMed
概括
此摘要是机器生成的。

scBoolSeq将单细胞RNA测序 (scRNA-seq) 数据与细胞命运的布尔模型联系起来. 它使scRNA-seq的准确二元化和生成现实的合成数据成为可能,改善了模型推断和基准测试.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
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科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 基因组学就是基因组学.

背景情况:

  • 布尔网络模型基因/转录因子激活状态随着时间的推移.
  • 在数据驱动的生物模型中,将定性布尔态与定量 scRNA-seq 数据相结合至关重要.
  • scRNA-seq二元化和合成数据生成是布尔模型推断和验证的关键.

研究的目的:

  • 开发一种用于scRNA-seq数据和布尔基因激活状态的双向链接的方法.
  • 为了实现scRNA-seq数据集的准确二元化,用于布尔模型推理.
  • 从布尔模型生成现实的合成scRNA-seq数据进行基准测试.

主要方法:

  • scBoolSeq分析了scRNA-seq数据,以对基因伪数分布进行分类 (单模,双模,零膨胀).
  • 它适用于脱学事件的基因依赖概率模型.
  • 该方法执行scrRNA-seq二元化,并通过偏向采样和放弃模拟生成合成数据.

主要成果:

  • scBoolSeq成功对scRNA-seq数据进行二元化,并从布尔痕迹生成合成数据集.
  • 一个案例研究证明了scBoolSeq在数据驱动的布尔模型推理中的实用性.
  • 与BoolODE相比,scBoolSeq的合成数据更好地重现了真正的scRNA-seq统计数据 (平均变异,平均下降).

结论:

  • scBoolSeq提供了一个强大的框架,用于将scRNA-seq数据与布尔模型集成.
  • 该方法提高了布尔模型推理的准确性和推理算法的基准测试.
  • scBoolSeq促进了一种更多的定量和数据驱动的方法来理解细胞命运动态.