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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: Jul 15, 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.

bioRxiv : the preprint server for biology

|September 25, 2023

概括

此摘要是机器生成的。

这项研究引入了用于长读测序的新型局部单质型近似,提高了跨PacBio和牛津纳米孔技术等多种平台的变异调用准确性. 这种方法提高了DeepVariant的性能.

关键词:

深度变体变体长时间阅读一个纳米孔.这就是PacBio HiFi.这就是PacBio Revio.变种调用调用的变种

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

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

相关实验视频

Last Updated: Jul 15, 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

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

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

科学领域:

基因组学和生物信息学
下一代测序技术的新一代技术

背景情况:

长读数测序在具有挑战性的基因组区域中促进了变异检测,并有助于临床遗传诊断.
新兴的第三代测序平台 (太平洋生物科学,牛津纳米孔技术) 提供了先进的功能,但需要可适应的变异调用方法.
基于深度神经网络的变异调用者利用局部单元类型识别来提高长读的基因类型精度.

研究的目的:

开发一种高效的局部哈普洛型方法,用于调用长读测序数据的变体.
提高变量调用工具的适应性,以适应新的测序平台和数据类型.
通过使用长读序列来提高变体检测的准确性和可扩展性.

主要方法:

开发一个局部单 haplotype 的近似算法.
将近似方法集成到DeepVariant变体调用框架中.
跨多个长读测序平台的验证,包括PacBio Revio和牛津纳米孔技术 (R10.4简单和双重数据).

主要成果:

在经过测试的平台上实现了最先进的变种调用性能.
通过局部单基因型近似证明了基因型准确度的提高.
建立了DeepVariant作为各种长读测序技术的通用变异调用解决方案.

结论:

开发的局部单元型近似显著提高了长读测序的变异调用性能.
这种方法为各种测序平台提供了可扩展和适应的解决方案.
增强的DeepVariant提供了一种通用方法,用于在临床和研究环境中准确检测遗传变异.