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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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Related Experiment Video

Updated: Apr 13, 2026

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.

Sean K Simmons^1,2,3, Xian Adiconis^4,5,6, Nathan Haywood^4,5,6

¹Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA. seankenneths@gmail.com.

|April 11, 2026

Summary

This summary is machine-generated.

This study enhances allele-specific expression (ASE) analysis for single-cell RNA sequencing (scRNA-seq). Methods were developed to improve ASE detection power, outperforming traditional eQTL analysis in Parkinson

Keywords:

Allele-specific expression Parkinson’s disease RNA-seq Single-cell Variant to function

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Last Updated: Apr 13, 2026

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Genomic variations influence RNA expression.
Single-cell RNA sequencing (scRNA-seq) enables studying gene expression at a high resolution.
Allele-specific expression (ASE) analysis reveals differences in expression between alleles.

Purpose of the Study:

To explore how experimental and computational factors affect ASE detection power in scRNA-seq.
To develop computational tools for single-cell ASE analysis.
To compare the power of ASE analysis with eQTL analysis using real-world data.

Main Methods:

Development of a computational tool suite for single-cell ASE analysis.
Utilizing single-nucleus RNA sequencing (snRNA-Seq) data.
Investigating the impact of read length and hybrid selection on ASE detection.
Application of methods to a Parkinson's disease cohort.

Main Results:

Single-nucleus RNA-Seq yields more ASE information from intronic than exonic regions.
Increased read length enhances ASE detection power.
Hybrid selection improves ASE detection power for targeted genes.
ASE analysis demonstrated higher power than eQTL analysis in a Parkinson's disease cohort.

Conclusions:

The developed computational tools and optimized experimental strategies significantly improve ASE analysis in single cells.
Single-cell ASE analysis is a powerful approach to understand the impact of genomic variation on gene expression, particularly in disease contexts like Parkinson's disease.