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Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data
Sean K Simmons1,2,3, Xian Adiconis4,5,6, Nathan Haywood4,5,6
1Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA. seankenneths@gmail.com.
Genome Biology
|April 11, 2026
Summary
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
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.
