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相关概念视频

Nucleic Acid Structure01:25

Nucleic Acid Structure

6.1K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
6.1K
RNA Stability01:53

RNA Stability

33.5K
Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
33.5K
Nucleic Acids02:43

Nucleic Acids

44.2K
Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
44.2K
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 Editing02:23

RNA Editing

9.0K
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...
9.0K
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

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相关实验视频

Updated: Jul 4, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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机器学习在RNA结构预测:进步和挑战.

Sicheng Zhang1, Jun Li1, Shi-Jie Chen2

  • 1Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri.

Biophysical journal
|February 1, 2024
PubMed
概括

机器学习模型对预测RNA结构,对生物功能至关重要,显示出希望. 这个视角涵盖了策略,在2D/3DRNA结构预测中的挑战,以及RNA语言模型.

科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学

背景情况:

  • RNA分子对于生物过程至关重要,其结构决定功能.
  • 使用机器学习预测蛋白质结构的进步为RNA结构预测提供了潜力.
  • 了解RNA结构是解读其多样化的生物作用的关键.

研究的目的:

  • 讨论开发用于RNA结构预测的机器学习模型的进展和障碍.
  • 探索构建这些模型的策略,并解决预测二级 (2D) 和三级 (3D) RNA结构的挑战.
  • 突出与创建RNA语言模型相关的好处和困难.

主要方法:

  • 审查目前用于RNA结构预测的机器学习技术.
  • 对RNA二级和三级结构预测的模型构建策略的分析.
  • 在结构预测的背景下对RNA语言模型的评估.

主要成果:

  • 机器学习方法越来越适用于RNA结构预测.
  • 预测二维和三维RNA结构存在特殊挑战,解决方案的开发正在进行中.
  • 在这个领域,RNA语言模型具有独特的优势和挑战.

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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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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

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相关实验视频

Last Updated: Jul 4, 2025

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

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结论:

  • 基于机器学习的模型将成为RNA结构预测的重要工具.
  • 这些工具将增强我们对RNA结构及其功能影响的理解.
  • 机器学习的持续研究将加速RNA生物学方面的发现.