MBASED

MBASED detects allele-specific gene expression (ASE) from RNA sequencing (RNA-seq) data by integrating allele counts across single nucleotide variant (SNV) loci into gene-level ASE measures without requiring prior phasing.

Key Features:

• SNV aggregation: Aggregates allele counts from multiple SNVs associated with a gene to produce a gene-level ASE measure.
• Phasing independence: Operates without requiring prior phasing information for input data.
• Analytical modes: Supports both one-sample and two-sample analyses.
• Power enhancement: Aggregation across SNVs increases statistical power and reliability for detecting ASE signals.
• Simulation validation: Demonstrates robust ASE identification in simulation studies.
• Cancer applications: Has been applied to cancer cell lines and paired tumor-normal samples, revealing extensive ASE in cancer samples.
• Copy number association: Attributes much of the observed differential ASE to genomic copy number alterations.

Scientific Applications:

• ASE detection from RNA-seq: Identification of allele-specific expression at the gene level using RNA-seq allele counts.
• Cancer genomics: Analysis of allele-specific expression in cancer cell lines and paired tumor-normal tissue comparisons.
• Regulatory mechanism investigation: Linking allele-specific expression to genomic copy number alterations to study gene regulation in disease contexts.
• Unphased dataset analysis: Enabling ASE studies on datasets that lack prior phasing information.

Methodology:

Aggregates allele counts from multiple SNVs per gene into a gene-level ASE measure, performs one-sample and two-sample analyses, operates without prior phasing, and evaluates performance using simulations.