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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 1, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

通过整合批量和scRNA-seq数据来实现细胞类型特定的eQTL优先级的统一框架.

Xinyi Yu¹, Xianghong Hu², Xiaomeng Wan²

¹Shenzhen Research Institute of Big Data, Shenzhen 518172, China; School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China.

American journal of human genetics

|January 17, 2025

概括

我们开发了IBSEP,这是一种新方法,集成了大量和单细胞RNA测序数据,以改善细胞类型特定的eQTL (ct-eQTL) 发现. 这种方法增强了对细胞水平上复杂疾病的遗传影响的理解.

关键词:

在GWAS中,GWAS就是GWAS.在eQTLs中使用.表达方式定量特征 loci loci 全基因组关联研究研究.这就是 scRNA-seqq.一个单细胞RNA测序.统计遗传学统计遗传学

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Published on: November 11, 2014

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Last Updated: Jun 1, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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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.
系统生物学系统生物学

背景情况:

全基因组关联研究 (GWAS) 识别复杂特征的遗传变异,但生物解释,特别是对于非编码区域,是困难的.
表达量的特征位置 (eQTL) 研究将基因变异与基因表达联系起来,有助于疾病基因识别.
大量RNA测序 (BulkRNA-seq) eQTL分析由于异质性而缺乏细胞分辨率,而单细胞RNA测序 (scRNA-seq) 提供更高的分辨率,但样本大小更小.

研究的目的:

介绍IBSEP,一个用于增强细胞类型特定eQTL (ct-eQTL) 优先级的新型统计框架.
整合大量RNA-seq和scRNA-seq数据,以克服单个方法的局限性.
改善复杂特征中特定于细胞类型的遗传调节机制的识别.

主要方法:

开发了一个统计框架,IBSEP,利用一个层次的线性模型.
综合总结统计数据来自大量RNA-seq和scRNA-seq数据.
通过广泛的模拟和对外围血液单核细胞和大脑皮层数据集的分析来验证该方法.

主要成果:

与现有的方法相比,IBSEP在识别ct-eQTL方面表现优越.
该框架有效地结合了批量和单细胞数据的优势,减轻了个人限制.
分析揭示了细胞类型特定的转录性调节机制.

结论:

IBSEP提供了ct-eQTL分析的强大方法,增强了GWAS发现的生物学解释.
该方法在细胞分辨率上提供了对复杂疾病遗传基础的更深入的见解.
这项工作有助于发现与人类健康和疾病相关的细胞类型特定遗传调节.