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相关概念视频

RNA-seq03:21

RNA-seq

12.2K
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...
12.2K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

12.0K
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,...
12.0K
Ribosome Profiling02:24

Ribosome Profiling

4.2K
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...
4.2K
RNA Editing02:23

RNA Editing

10.0K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
10.0K

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相关实验视频

Updated: Feb 26, 2026

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

Published on: February 2, 2024

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神经网络辅助的RNA速度归算,以加强基于转录动态的分析.

Riku Egami1, Momo Shirotori2, Takashi Tamura2

  • 1Chugai Pharmaceutical Co., Ltd., Research Division, Yokohama, Kanagawa, Japan.

iScience
|February 25, 2026
PubMed
概括

现有的RNA速度工具错过了许多基因. 我们的新方法,NARVI (神经网络辅助RNA速度输入),使用深度学习来估计这些基因的速度,改进基因表达分析.

关键词:
生物化学 生物化学生物计算方法生物计算方法神经网络的神经网络的神经网络

更多相关视频

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

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相关实验视频

Last Updated: Feb 26, 2026

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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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:

  • 对于理解基因转录动态来说,RNA速度分析至关重要.
  • 目前的RNA速度工具面临着局限性,无法估计许多基因的速度.
  • 这种差距限制了单细胞转录组学的全面下游分析.

研究的目的:

  • 开发一种新的深度学习框架,NARVI,用于准确的RNA速度推算.
  • 克服现有工具在估计基因速度方面的局限性.
  • 通过恢复以前无法计算的基因的速度来扩大下游分析的范围.

主要方法:

  • 纳维使用深度学习框架来学习表达速度关系.
  • 它利用可计算的基因来预测缺失估计的基因的速度.
  • 该方法在多个单细胞转录组数据集上进行了评估.

主要成果:

  • 纳维成功地估计了数千个以前无法计算的基因的速度.
  • 归算的速度使得增强的轨迹推断和标记基因分析成为可能.
  • 该框架在各种数据集中表现出强的性能.

结论:

  • NARVI显著扩大了基于RNA速度的下游分析的范围.
  • 这种归算方法为基因转录动态提供了更深入的见解.
  • 纳维代表了计算转录学学方面的重大进步.