EMASE – Expectation-Maximization Algorithm for Allele Specific Expression

EMASE is a software program written in Python to quantify allele-specific expression and gene expression simultaneously from RNA-seq data. EMASE takes in the diploid transcriptome alignment BAM file and GTF file as inputs and estimates expression abundance for each isoforms and each alleles using Expectation Maxmization algorithm.

Why Use EMASE?

Current RNA-seq analysis pipeline employ two steps to quantify gene expression and allele-specific expression (ASE); gene expression is estimated from all read alignments, while ASE is assessed separately by using only reads that overlap known SNP locations.

Large-scale genome sequencing efforts have characterized millions of genetic variants across in human and model organisms. However development of tools that can effectively utilize this individual/strain-specific variation to inform quantitation of gene expression abundance have lagged behind.

In F1 hybrids from model organisms, EMASE allows us to utilize parental strain-specific genetic variation in RNA-seq analysis to quantify gene expression and allele-specific expression (ASE) simultaneously

In humans, EMASE allows us to utilize the individual’s genetic variation in doing personalized RNA-seq analysis and quantify gene expression and allele-specific expression (ASE) simultaneously

Briefly, EMASE: EM for allele-specific expression, uses individualized diploid genomes/transcriptomes adjusted for known genetic variations and quantifies allele-specific gene expression and total gene expressionsimultaneously. The EM algorithm employed in EMASE models multi-reads at the level of gene, isoform, and allele and apportions them probabilistically.

Availability – EMASE is available at: https://pypi.python.org/pypi/emase/0.9.0

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