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相关概念视频

RNA-seq03:21

RNA-seq

10.1K
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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Next-generation Sequencing03:00

Next-generation Sequencing

91.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Sanger Sequencing01:57

Sanger Sequencing

755.1K
DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
755.1K

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相关实验视频

Updated: Jul 24, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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一个基于深度学习的RNA-seq生殖系变体呼叫者.

Daniel E Cook1, Aarti Venkat2, Dennis Yelizarov1

  • 1Google LLC., Mountain View, CA 94043, USA.

Bioinformatics advances
|July 7, 2023
PubMed
概括
此摘要是机器生成的。

深度学习工具DeepVariant现在可以从RNA测序数据中准确地调用遗传变异. 这种增强的模型克服了常见的RNA测序错误,优于现有的变体调用器.

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相关实验视频

Last Updated: Jul 24, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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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.
  • 计算生物学 计算生物学

背景情况:

  • RNA测序 (RNA-seq) 是用于基因表达,QTL发现和融合事件识别的多功能.
  • RNA-seq数据呈现出独特的错误来源,如可变的转录丰度,使生殖系变异检测复杂化.
  • 现有的变异调用者与RNA-seq数据复杂性作斗争.

研究的目的:

  • 为了适应DeepVariant,一个深度学习变体调用器,从RNA-seq数据准确调用变体.
  • 开发一种能够学习和减轻RNA-seq特定错误源的模型.
  • 根据既有方法评估增强的DeepVariant模型的性能.

主要方法:

  • 扩展DeepVariant深度学习框架以处理RNA序列数据.
  • 训练模型识别和纠正RNA测序中固有的错误.
  • 与现有的变种呼叫者 (如白和GATK.等) 的比较分析.

主要成果:

  • 该DeepVariant RNA-seq模型从RNA测序数据中实现变异调用的高准确性.
  • 与白和GATK相比,该模型表现出优越的性能.
  • 分析确定了影响准确性和模型处理RNA编辑事件的能力的因素.

结论:

  • 可以有效地扩展DeepVariant,用于准确的变异调用RNA测序.
  • 开发的模型比目前的RNA-seq变异调用器提供了更好的准确性和稳定性.
  • 进一步的门策略可以优化生产管道的模型.