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RNA-seq

RNA-seq

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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Updated: Jun 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

局部读取哈普标记使得准确的长读取小变体调用能够实现.

Alexey Kolesnikov¹, Daniel Cook¹, Maria Nattestad¹

¹Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.

Nature communications

|July 13, 2024

概括

此摘要是机器生成的。

我们开发了一种新方法来简化长时间读取的测序变异调用. 这种方法可以提高各种平台的准确性,如PacBio和牛津纳米孔技术,有助于基因诊断.

更多相关视频

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

相关实验视频

Last Updated: Jun 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

科学领域:

基因组学和生物信息学
计算生物学计算生物学
下一代测序的测序方法

背景情况:

长读测序技术 (例如,太平洋生物科学,牛津纳米孔技术) 对于复杂的基因组区域的变异检测和临床遗传诊断至关重要.
基于深度神经网络的变异调用者利用本地单元型信息来提高长读数的基因型准确性.
目前使用本地哈普洛类型的方法引入了计算开销,阻碍了新测序平台和数据类型的可扩展性.

研究的目的:

为长读变异调用开发一种高效的局部单元型近似方法.
改进跨多种测序平台的变异调用的适应性和性能.
为了简化将本地单双型信息集成到现有的变体调用框架中,如DeepVariant.

主要方法:

开发一种新的局部单 haplotype 的近似算法.
将近似方法集成到DeepVariant变种调用管道中.
在太平洋生物科学 (PacBio Revio) 和牛津纳米孔技术 (ONT R10.4简单和双重) 的数据上的基准性能.

主要成果:

局部单元型近似方法实现了最先进的变异调用性能.
该方法在多个长时间读取的测序平台上显示出高准确性,包括PacBio Revio和ONT R10.4数据.
变量调用工作流程的显著简化,与传统的局部单质类型化相比,减少了计算开销.

结论:

开发的局部单元型近似方法有效地提高了长时间读取的测序数据的变异调用精度和效率.
这种简化方法可以更容易地将高级变异调用应用到新兴的测序技术和数据类型.
该方法提供了一个强大的解决方案,用于改进使用长读序列的遗传诊断和基因组研究.