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相关概念视频

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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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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相关实验视频

Updated: Jul 12, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA
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A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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DeepRaccess:使用深度学习进行高速RNA可访问性预测.

Kaisei Hara1,2, Natsuki Iwano1, Tsukasa Fukunaga3

  • 1Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan.

Frontiers in bioinformatics
|October 26, 2023
PubMed
概括
此摘要是机器生成的。

新的深度学习工具DeepRaccess准确地预测了E.coli中的RNA可访问性和蛋白质丰度. 与传统方法相比,它为转录组规模分析提供了显著的加速.

关键词:
在RNA的可访问性方面.RNA的二级结构是RNA的二级结构.加速加速加速的加速.机器学习是机器学习.翻译效率预测 翻译效率预测

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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相关实验视频

Last Updated: Jul 12, 2025

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A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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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:

  • RNA二次结构,特别是RNA可访问性,对于预测RNA-RNA相互作用和 prokaryotes 中的翻译效率至关重要.
  • 像Raccess这样的现有工具是计算密集型的,限制了它们在大规模转录组分析中的应用.

研究的目的:

  • 开发一个计算效率高的深度学习模型,DeepRaccess,用于预测RNA可访问性.
  • 评估DeepRaccess的准确性与既定方法相比,以及其预测蛋白质丰富度等生物结果的能力.

主要方法:

  • 使用深度学习开发DeepRaccess,在人工RNA序列上进行训练.
  • 使用模拟和经验数据集对Raccess计算进行验证的预测.
  • 评估了DeepRaccess与E.coli在开始代码附近的蛋白质丰度之间的相关性.

主要成果:

  • 深度访问预测显示与Raccess计算的可访问性有很高的相关性.
  • 该模型在从RNA序列中预测大肠杆菌蛋白质丰度时表现出中等准确性.
  • 在GPU环境中实现了显著的计算加速度 (几十到几百倍).

结论:

  • DeepRaccess为RNA可访问性预测提供了一个准确且计算效率高的替代方案.
  • 该工具在预测翻译效率和蛋白质丰度方面具有潜在的应用.
  • 自由可用的源代码和模型促进了更广泛的研究使用.