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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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RNA-seq03:21

RNA-seq

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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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相关实验视频

Updated: Jun 26, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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最新RNArt:对RNA3D结构预测的当前方法进行基准测试.

Clément Bernard1,2, Guillaume Postic1, Sahar Ghannay2

  • 1Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France.

NAR genomics and bioinformatics
|May 15, 2024
PubMed
概括
此摘要是机器生成的。

预测RNA3D结构对于理解RNA功能至关重要. 本研究审查了包括深度学习在内的计算方法,并对其在RNA-Puzzles数据集上的性能进行了基准测试.

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相关实验视频

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科学领域:

  • 计算生物学 计算生物学
  • 结构生物学 结构生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • RNA分子执行重要的生物功能,需要了解它们的三维 (3D) 结构.
  • 计算方法已经开发了二十多年来,从序列预测RNA的3D形状.
  • 现有的方法,分类为*ab initio*或基于模板的方法,需要提高性能.

研究的目的:

  • 对RNA3D结构预测的现有计算方法进行审查.
  • 在这个领域评估新型深度学习方法的性能.
  • 提供当前工具的基准,并促进未来的研究.

主要方法:

  • 对*ab initio*,基于模板和深度学习RNA 3D结构预测方法的审查.
  • 使用RNA-Puzzles数据集对九种不同的计算工具进行基准测试.
  • 开发一个在线仪表板来可视化预测结果.

主要成果:

  • 深度学习方法显示出希望,但在RNA3D结构预测方面面临挑战.
  • 该基准提供了九种最先进的方法的比较分析.
  • 一个可访问的在线平台 (EvryRNA) 可用于探索预测结果.

结论:

  • 准确的RNA3D结构预测仍然是一个具有挑战性但至关重要的研究领域.
  • 基准和在线仪表板为科学界提供了宝贵的资源.
  • 需要进一步开发计算方法,特别是深度学习,以提高预测准确度.