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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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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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相关实验视频

Updated: Jun 3, 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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系统地对深度学习方法进行基准测试,以预测三级RNA结构.

Akash Bahai1, Chee Keong Kwoh2, Yuguang Mu1

  • 1School of Biological Sciences (SBS), Nanyang Technological University, Singapore, Singapore.

PLoS computational biology
|January 8, 2025
PubMed
概括

这项研究对RNA3D结构预测的深度学习方法进行了基准测试,发现ML方法的表现优于其他方法. DeepFoldRNA和DRFold显示出最好的结果,尽管预测非沃森-克里克对仍然是一个挑战.

科学领域:

  • 计算生物学 计算生物学
  • 结构生物学 结构生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 了解RNA3D结构对于RNA生物学至关重要.
  • 实验性结构确定是昂贵和耗时的.
  • 计算方法,包括机器学习 (ML),提供快速的RNA结构预测.

研究的目的:

  • 系统地对RNA3D结构预测的最先进的深度学习方法进行基准测试.
  • 确定影响预测准确性的因素,如RNA多样性,序列长度和MSA质量.
  • 将基于ML的方法与非ML方法进行比较.

主要方法:

  • 在各种数据集上对深度学习RNA结构预测工具的基准测试.
  • 基于RNA特征和数据质量的性能变化分析.
  • 评估诸如MSA质量和二次结构预测等因素.

主要成果:

  • 基于ML的方法通常在RNA结构预测方面优于非ML方法.
  • 新型或合成RNA的性能差异较小.
  • 多次序对齐 (MSA) 质量显著影响预测准确性.
  • 大多数方法都很难预测非沃森-克里克基数对.

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  • 在自动化方法中,DeepFoldRNA和DRFold表现出最好的预测性能.

    • 结论:

    • 深度学习显示了RNA3D结构预测的前景,其中DeepFoldRNA和DRFold等特定方法领先.
    • MSA质量和二次结构预测是准确的3DRNA结构预测的关键因素.
    • 未来的研究应该专注于改善非沃森-克里克对预测和各种RNA类型的整体准确性.