asSeq

asSeq performs allele-specific analysis of RNA-seq and ChIP-seq next-generation sequencing data by integrating allele-specific expression (ASE) and total read count (TReC) for cis- and trans-eQTL mapping.

Key Features:

• Allele-Specific Expression (ASE) Analysis: Leverages RNA-seq and integrates total read count (TReC) with ASE measurements to resolve allele-specific expression and distinguish cis- and trans-eQTL.
• Enhanced eQTL Mapping: Employs a direct modeling approach using discrete distributions for TReC data, providing higher statistical power compared to two-step normalization followed by linear regression.
• Cost-Effective Experimental Design: Combines TReC and ASE data to enable reduced sample sizes with increased sequencing depth per sample while maintaining statistical power for cis-eQTL studies.
• Versatility Across Data Types: Extends the framework beyond RNA-seq to other sequencing datasets such as ChIP-seq and supports single-gene association-based eQTL mapping with groundwork for linkage-based and multi-marker/multi-gene studies.

Scientific Applications:

• Genetic Basis of Gene Expression: Identifies genetic variants that influence gene expression levels by integrating ASE and TReC measurements.
• Disease Association Studies: Pinpoints specific alleles associated with differential gene expression to inform genetic investigations of disease mechanisms.
• Functional Genomics Research: Dissects cis- and trans-eQTLs to clarify regulatory networks underlying genome function.

Methodology:

asSeq implements a unified statistical framework that models TReC with discrete distributions and integrates ASE data to directly infer cis- and trans-eQTLs, in contrast to two-step normalization followed by linear regression.