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相关概念视频

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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相关实验视频

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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一种深度学习驱动的采样技术,用于探索RNA干环的相空间.

Ayush Gupta1, Heng Ma2, Arvind Ramanathan2

  • 1William A. Brookshire Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States.

Journal of chemical theory and computation
|October 7, 2024
PubMed
概括

深度学习方法DeepDriveMD (DDMD) 通过从模拟中进行自适应式学习,有效地研究RNA干循环折叠. 这种方法克服了传统方法的局限性,使得可以在降低计算成本的情况下准确地估计自由能源景观.

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科学领域:

  • 计算生物学 计算生物学
  • 生物物理学的生物物理.
  • 机器学习 机器学习

背景情况:

  • RNA干环折叠至关重要,但由于崎的能源景观,在计算上具有挑战性.
  • 传统的罕见事件采样方法具有诸如高计算成本或需要先前知识等局限性.

研究的目的:

  • 适应DeepDriveMD (DDMD),一种深度学习技术,用于高效的RNA干环折叠模拟.
  • 为了克服研究RNA折叠动态的计算障碍.

主要方法:

  • 适应的DeepDriveMD (DDMD) 用于使用通用联系地图作为输入的RNA干循环折叠.
  • 使用低维潜态表示的飞行学习来指导模拟.
  • 使用恒温框架,没有外部偏差潜力.

主要成果:

  • DDMD准确地估计了在室温下RNA干环自由能量景观.
  • 与具有偏差潜力的模拟相比,实现了显著较低的计算成本.
  • 证明学习的潜伏空间捕捉了RNA折叠的相关缓慢自由度.

结论:

  • DDMD是研究RNA干环折叠的有效和计算效率高的方法.
  • 适应性学习策略和潜在空间表示加速了对相关构造空间的探索.
  • 这项工作提供了一个框架和决策指南,用于将DDMD应用于其他罕见事件采样问题.