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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 2, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Clair3-RNA:基于深度学习的小变体调用器,用于长读RNA测序数据的长读RNA测序数据.

Zhenxian Zheng¹, Xian Yu¹, Lei Chen¹

¹Department of Computer Science, School of Computing and Data Science, University of Hong Kong, Hong Kong, China.

bioRxiv : the preprint server for biology

|January 13, 2025

概括

Clair3-RNA是一个新的深度学习工具,用于长读RNA测序变异调用,在PacBio和ONT平台上实现高精度. 它有效地区分了RNA编辑部位与遗传变异.

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

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Last Updated: Jun 2, 2025

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Published on: February 2, 2024

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

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:

长读RNA测序 (lrRNA-seq) 能够实现全长的异构体和基因表达分析,但由于高错误率和转录复杂性而面临挑战.
准确的变异调用lrRNA-seq数据对于理解转录多样性和RNA编辑事件至关重要.

研究的目的:

推出Clair3-RNA,这是第一个基于深度学习的变异调用器,专门为lrRNA-seq数据设计.
通过解决 lrRNA-seq 特定挑战,如不均的覆盖面和 RNA 编辑来提高变异调用性能.

主要方法:

开发Clair3-RNA,这是一个深度学习模型,利用Clair系列管道与lrRNA-seq特定优化.
技术的实施包括不均覆盖范围的规范化,精细的培训数据,编辑站点发现和哈普洛型分阶段.
在PacBio和牛津纳米孔技术 (ONT) 平台上的评估,包括cDNA和直接RNA测序 (dRNA).

主要成果:

克莱尔3-RNA获得了高SNP F1分数:在ONT dRNA004上达到~91%;在PacBio Iso-Seq/MAS-Seq上达到~92% (读数≥4).
性能提高到95% (ONT) 和96% (PacBio) 在≥10次阅读时,以及97% (ONT) /98% (PacBio) 在分阶段阅读时.
在GIAB样本上与现有呼叫者相比,表现出优越的性能,并准确地区分RNA编辑站点.

结论:

克莱尔3-RNA为在不同平台上调用lrRNA-seq数据的变异提供了强大而准确的解决方案.
该工具处理RNA编辑事件的能力增强了其用于全面转录组分析的实用性.
Clair3-RNA是一个开源资源,促进进一步研究RNA变异调用.