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相关概念视频

RNA-seq03:21

RNA-seq

10.4K
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: Sep 9, 2025

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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使用合成RNA来基准从直接RNA测序中推断多元A长度

Jessie J-Y Chang1, Xuan Yang1, Haotian Teng2

  • 1Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, 3000, Australia.

GigaScience
|September 3, 2025
PubMed
概括

我们使用直接的RNA测序数据对四种多种类型的尾巴估计工具进行了比较. 多拉多显示出最好的性能,提供快速运行时间和高精度的转录组分析.

关键词:
牛津纳米孔技术直接的RNA测序估计情况多种类型的尾分段化

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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科学领域:

  • 分子生物学
  • 生物信息学
  • 基因组学

背景情况:

  • 多基化对RNA调节至关重要,影响mRNA衰变,转化和异形特异性.
  • 直接RNA测序提供了全长RNA分析,使得全转录组的多A尾研究成为可能.
  • 现有的多项尾部估计工具缺乏与黄金标准数据集进行全面的比较.

研究的目的:

  • 介绍一个新的深度学习工具BoostNano.
  • 与已知工具 (tailfindr,nanopolish) 和深度学习工具 (Dorado) 进行比较.
  • 使用已知多个A尾长的合成RNA标准来评估工具性能.

主要方法:

  • 开发BoostNano深度学习模型用于多项A) 估计.
  • 在合成RNA数据集 (Sequin,eGFP) 上评估四种工具 (BoostNano,tailfindr,nanopolish,Dorado).
  • 基于已知地面真相的工具精度分析.

主要成果:

  • 工具的性能取决于尾的长度和样品的类型.
  • 在多次读取中平均化多项估计提高了准确性.
  • 多拉多表现出卓越的性能,运行时间快,平均误差低,使用方便.

结论:

  • 在直接RNA测序中推使用Dorado测量.
  • 准确的多A尾分析对于理解转录稳定性和调节至关重要.
  • 这一基准为改进转录组分析和RNA调控研究提供了参考.