Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

RNA Structure01:23

RNA Structure

72.6K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
72.6K
RNA Stability01:53

RNA Stability

33.9K
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...
33.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
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...
10.4K
Experimental RNAi02:15

Experimental RNAi

6.3K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.3K
RNA Editing02:23

RNA Editing

9.2K
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...
9.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Programming T cells for intercellular genome editing.

bioRxiv : the preprint server for biology·2026
Same author

Harmonizing standards and resources for the medical genome.

Nature·2026
Same author

A family of archaeal hibernation factors that bind in tandem and protect ribosomes in dormant cells.

Nature communications·2026
Same author

Publisher Correction: Lung and liver editing by lipid nanoparticle delivery of a stable CRISPR-Cas9 ribonucleoprotein.

Nature biotechnology·2026
Same author

Targeting Cancer-Specific Mutations with RNA-Triggered Chromatin Shredding.

Nature·2026
Same author

Selective Elimination of TP53 Mutant Cells by Transcript-Activated Chromatin Shredding.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Sep 15, 2025

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.7K

通过扩展训练数据改进RNA二次结构预测.

Conner J Langeberg1,2,3, Taehan Kim1,2,3,4,5,6,7,8,9, Roma Nagle3

  • 1Innovative Genomics Institute; University of California, Berkeley, CA, USA.

bioRxiv : the preprint server for biology
|July 14, 2025
PubMed
概括
此摘要是机器生成的。

一个新的数据集,RNASSTR,通过深度学习改进了RNA二次结构预测. 用这些数据重新训练SincFold,提高了它对未见的RNA家族的准确性,克服了当前RNA结构建模的局限性.

更多相关视频

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

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

Published on: December 9, 2022

4.5K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.8K

相关实验视频

Last Updated: Sep 15, 2025

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.7K
Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

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

Published on: December 9, 2022

4.5K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.8K

科学领域:

  • 计算生物学 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:

  • 深度学习已经推进了蛋白质结构预测,但RNA结构预测滞后.
  • 目前的RNA结构模型缺乏蛋白质模型的准确性,可能是由于训练数据有限.
  • 现有的方法显示中等成功,但没有实现RNA二级和三级结构的高精度.

研究的目的:

  • 开发用于RNA结构预测的大型,多样化的数据集,以解决数据限制.
  • 评估这种增强数据集对RNA结构预测深度学习模型的影响.
  • 为了提高de novoRNA二次结构预测的准确性和概括性.

主要方法:

  • 开发RNASSTR数据集,包括配对RNA序列和二次结构.
  • 使用RNASSTR数据集对两个深度学习模型SincFold和MXfold2进行重新训练.
  • 对未见的RNA家族的模型性能和概括性的评估.

主要成果:

  • 在对RNASSTR.重新训练后,SincFold证明了对新RNA家族的改进泛化.
  • 增强的数据集提高了SincFold准确的de novoRNA二次结构预测的能力.
  • 重新训练MXfold2在计算上是昂贵的,并且在测试集上没有产生高性能.

结论:

  • RNASSTR数据集代表了RNA结构建模的重大进步.
  • 这一数据集为开发未来RNA二次结构预测算法提供了坚实的基础.
  • 解决数据限制对于提高RNA结构预测准确度至关重要.