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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

3.5K
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...
3.5K
RNA-seq03:21

RNA-seq

10.0K
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...
10.0K
Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

10.6K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
10.6K

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相关实验视频

Updated: Jul 7, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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从快照RNA数据中推断基因表达模型,使用贝叶斯非参数.

Zeliha Kilic1,2, Max Schweiger3,4,2, Camille Moyer3,5

  • 1Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.

Nature computational science
|December 21, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯方法,用于从单分子RNA计数同时推断基因表达模型及其参数. 这种方法增强了对细胞调节网络和转录动态的理解.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

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相关实验视频

Last Updated: Jul 7, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

37.4K
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

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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
  • 分子生物学分子生物学

背景情况:

  • 基因表达模型对于理解细胞调节反应和单细胞转录动态至关重要.
  • 当前的计算方法需要对基因状态和连接性进行预规范,限制对模型和参数的同时推断.

研究的目的:

  • 开发一种新的计算方法,用于对基因表达模型的同时贝叶斯推断,包括基因状态,连接性和速率参数,直接从单分子RNA计数.
  • 解决现有框架的局限性,需要预定义的模型结构.

主要方法:

  • 提出了贝叶斯的非参数方法来学习基因状态,连接性和速率参数的完整分布.
  • 将基因表达模型作为随机变量来传播RNA计数中的噪音.
  • 开发了一个自相一致的推理框架.

主要成果:

  • 成功证明了对大肠杆菌 lacZ和Saccharomyces cerevisiae STL1通路的方法.
  • 使用合成数据验证了开发方法的稳定性.
  • 启用了复杂的基因表达模型的同时和自我一致的推断.

结论:

  • 拟议的方法在从单细胞RNA数据推断基因表达模型方面取得了重大进展.
  • 这种方法通过同时学习模型组件,可以更全面地了解细胞调节机制.
  • 该方法在不同的生物系统和合成数据中的稳定性表明其广泛适用性.