Computational methods for allele-specific expression in single cells.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Allele-specific expression (ASE) provides insights into gene regulation, imprinting, and cis-regulatory effects.
• Single-cell RNA sequencing (scRNA-seq) allows for ASE analysis at the individual cell level, offering unprecedented resolution.
• Existing methods for analyzing ASE from scRNA-seq data require comprehensive review and optimization.

Purpose of the Study:

• To review and highlight computational methods for processing and analyzing single-cell ASE data.
• To discuss statistical approaches for detecting allelic imbalance and its variability in scRNA-seq datasets.
• To outline methods for addressing specific biological questions using single-cell ASE and suggest future research directions.

Main Methods:

• Description of a bioinformatics pipeline for generating ASE counts from raw sequencing reads.
• Discussion of statistical methods for identifying allelic imbalance and its variation across different conditions.
• Overview of specialized methods leveraging single-cell ASE for targeted biological investigations.

Main Results:

• The review synthesizes current computational and statistical methodologies for single-cell ASE analysis.
• It identifies key challenges and areas for improvement in existing pipelines and statistical models.
• It highlights the utility of single-cell ASE in dissecting complex molecular mechanisms.

Conclusions:

• There is a need for integrated and optimized bioinformatics pipelines for single-cell ASE data.
• Further development of statistical methods is crucial to accommodate diverse scRNA-seq technologies.
• Single-cell ASE analysis holds significant potential for advancing our understanding of gene regulation and cellular heterogeneity.