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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...

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相关实验视频

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DTMP-prime:一个基于深度变压器的模型,用于预测主要编辑效率和PegRNA活动.

Roghayyeh Alipanahi1, Leila Safari1, Alireza Khanteymoori2

  • 1Department of Computer Engineering, University of Zanjan, Zanjan, Iran.

Molecular therapy. Nucleic acids
|December 10, 2024
PubMed
概括

本研究介绍了DTMP-Prime,这是一种深度变压器模型,通过分析主要编辑指导RNA (PegRNA) 活动来预测主要编辑 (PE) 效率. 这种工具有助于设计有效的PegRNAs,用于精确的基因组工程,并最大限度地减少非目标突变.

关键词:
克里斯普尔是什么意思?克里斯普尔是什么意思?DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERT DNABERMT: 生物信息学 生物信息学这就是PegRNA的PegRNA.深度学习是一种深度学习.远离目标 - 远离目标主编辑主要编辑.转移学习学习转移学习

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科学领域:

  • 基因组学就是基因组学.
  • 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:

  • 主编辑 (PE) 是一种基于CRISPR的强大基因组工程技术,用于纠正突变.
  • 优化主要编辑指导RNA (PegRNA) 对于实现高编辑效率至关重要.
  • 准确预测PE效率和非目标效应对于临床应用至关重要.

研究的目的:

  • 开发一个基于深度变压器的模型,DTMP-Prime,用于预测主要编辑效率.
  • 为了促进PegRNAs和ngRNAs的合理设计,以提高主要编辑结果.
  • 在基于CRISPR的基因组编辑实验中,提高非目标部位的预测准确度.

主要方法:

  • 一个基于变压器的深度学习模型是使用广泛的原始编辑数据构建的.
  • 提取和编码了PegRNAs和目标DNA序列的特征.
  • 基于DNABERT的嵌入和多头注意力框架被整合起来,以增强预测能力.
  • 模型性能使用皮尔森和斯皮尔曼相关系数进行评估.

主要成果:

  • DTMP-Prime在预测主要编辑效率和结果方面表现出高准确度.
  • 该模型显示,与现有的方法相比,对非目标站点的预测能力有所改善.
  • DTMP-Prime在不同PE模型和细胞系中表现出强大的通用性.
  • 评估证实DTMP-Prime在预测PE效率方面优于最先进的模型.

结论:

  • DTMP-Prime是一个有效的工具,用于预测prime编辑效率和PegRNA活动.
  • 开发的模型有助于设计精确和高效的主要编辑策略.
  • 在基因组工程中,DTMP-Prime为最大限度地减少非目标突变提供了一个有希望的方法.