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相关概念视频

RNA-seq03:21

RNA-seq

11.7K
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...
11.7K
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: Jan 7, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

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降解RNA-Seq样本的高真实性转录组重建,使用Denoising扩散模型.

Ke Xiao1, Jinlei Sun2, Yunqing Liu2

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China.

Biology
|December 30, 2025
PubMed
概括
此摘要是机器生成的。

DiffRepairer是一个新的深度学习工具,可以从退化的RNA样本中恢复准确的转录组数据. 这种计算方法通过逆转降解偏差来增强RNA测序分析.

关键词:
降解RNA的降解RNA的降解在RNA测序过程中,RNA测序变压器变压器变压器生物信息学是一种生物信息学.深度学习是一种深度学习.扩散模型的扩散模型.转录基因组修复

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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相关实验视频

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 存档样本中的RNA降解会导致RNA测序 (RNA-seq) 数据中的系统偏差.
  • 这限制了下游分析的准确性,需要强大的计算解决方案.
  • 高保真性转录组恢复对于可靠的生物解释至关重要.

研究的目的:

  • 从退化的RNA-seq数据中开发一种高保真性转录组恢复的计算方法.
  • 介绍DifRepairer,一个旨在逆转RNA降解效应的深度学习模型.
  • 为了验证DiffRepairer在恢复生物学上有意义的信号方面的有效性.

主要方法:

  • 推出了DiffRepairer,这是一个集成变压器架构和条件扩散模型的深度学习模型.
  • 在模拟的"退化-原始"配对数据上训练模型,用于一步修复映射.
  • 利用一个全面的模拟管道来生成训练数据.

主要成果:

  • DiffRepairer在五个不同的伪降级数据集中表现出稳定和卓越的性能.
  • 超过了传统的统计方法 (例如,CQN) 和标准的深度学习模型 (例如,VAE).
  • 在转录组修复的关键技术和生物指标上取得了更好的结果.

结论:

  • DiffRepairer是一款经过验证的,高精度的转录组修复工具.
  • 从降解的RNA-seq数据中有效地恢复生物意义信号.
  • 突出了生物信息学中先进的生成模型在数据恢复方面的潜力.