EMASE

EMASE estimates allele-specific expression (ASE) from RNA sequencing (RNA-seq) data by aligning reads to a diploid transcriptome and allocating multi-mapping reads hierarchically.

Key Features:

• Hierarchical Read Allocation: Resolves read-mapping ambiguities by prioritizing allocation among genes, then isoforms, and finally alleles.
• Handling Multi-Mapping Reads: Uses a weighted allocation strategy to assign multi-mapping reads rather than discarding them.
• Incorporation of Genetic Variants: Aligns reads to a diploid transcriptome that incorporates known genetic variants to distinguish allelic origin.
• Improved ASE Estimation: Enhances accuracy of allele-specific expression estimates compared to traditional reference genome alignments by leveraging hierarchical allocation of multi-read data.
• Comprehensive Expression Analysis: Provides estimates for total gene expression and isoform usage in addition to ASE.

Scientific Applications:

• Genetic Research: Detects widespread ASE associated with genetic polymorphisms and cis-acting variants to study genetic effects on expression.
• Parent-of-Origin Effects: Identifies parent-of-origin biases relevant to genomic imprinting and epigenetic regulation.
• Model Organism Studies: Analyzes RNA-seq data from F1 hybrid mice to resolve allele-specific expression patterns influenced by parental alleles.

Methodology:

Implements an Expectation-Maximization algorithm that iteratively refines expression and ASE estimates by aligning reads to a diploid transcriptome incorporating known variants and hierarchically allocating weighted multi-mapping reads across genes, isoforms, and alleles.