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相关概念视频

RNA-seq03:21

RNA-seq

9.8K
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...
9.8K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.0K
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...
6.0K
Types of RNA01:23

Types of RNA

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
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RNA Stability01:53

RNA Stability

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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...
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Experimental RNAi02:15

Experimental RNAi

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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...
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RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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相关实验视频

Updated: Jun 11, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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Wfold:一种使用深度学习预测RNA二次结构的新方法.

Yongna Yuan1, Enjie Yang1, Ruisheng Zhang1

  • 1School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China.

Computers in biology and medicine
|September 28, 2024
PubMed
概括

深度学习方法Wfold准确地预测RNA二级结构,在家族内数据集上表现优于传统技术,并可靠地预测伪结.

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.
  • 基因组学就是基因组学.

背景情况:

  • 准确的RNA二次结构预测对于理解非编码RNA功能至关重要.
  • 传统的方法依赖于热力学和自由能量的最小化,这是知识密集型和费力的.
  • 现有的方法与复杂的结构 (如伪结) 相斗争.

研究的目的:

  • 介绍Wfold,一种端到端的深度学习方法,用于RNA二次结构预测.
  • 用传统和最先进的方法来评估Wfold的表现.
  • 评估Wfold在预测伪结的能力.

主要方法:

  • Wfold使用类似图像的RNA序列表示.
  • 它采用了与变压器编码器集成的增强U-net架构.
  • 该模型以注释数据和基配对规则进行训练,利用远程依赖的自我注意力和局部特征的U-net.

主要成果:

  • 在不同RNA家族中,Wfold的性能与传统方法相美.
  • 它在家族内RNA数据集上显著优于最先进的方法.
  • Wfold证明了RNA二次结构的可靠预测,包括伪结.
关键词:
深度学习是一种深度学习.像图像这样的表示表示.预测RNA的二次结构自我注意力机制机制乌内特网络 乌内特网络

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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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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:

  • Wfold为RNA二次结构预测提供了基于深度学习的强大替代方案.
  • 该方法提高了准确性,特别是在家庭内预测和伪结预测方面.
  • 在改善RNA序列对齐,功能注释和结构建模方面,Wfold具有潜在的应用.