MOTIVATION: Allele-specific expression (ASE) refers to the differential 
abundance of the allelic copies of a transcript. RNA sequencing (RNA-seq) can 
provide quantitative estimates of ASE for genes with transcribed polymorphisms. 
When short-read sequences are aligned to a diploid transcriptome, read-mapping 
ambiguities confound our ability to directly count reads. Multi-mapping reads 
aligning equally well to multiple genomic locations, isoforms or alleles can 
comprise the majority (>85%) of reads. Discarding them can result in biases and 
substantial loss of information. Methods have been developed that use weighted 
allocation of read counts but these methods treat the different types of 
multi-reads equivalently. We propose a hierarchical approach to allocation of 
read counts that first resolves ambiguities among genes, then among isoforms, 
and lastly between alleles. We have implemented our model in EMASE software 
(Expectation-Maximization for Allele Specific Expression) to estimate total gene 
expression, isoform usage and ASE based on this hierarchical allocation.
RESULTS: Methods that align RNA-seq reads to a diploid transcriptome 
incorporating known genetic variants improve estimates of ASE and total gene 
expression compared to methods that use reference genome alignments. Weighted 
allocation methods outperform methods that discard multi-reads. Hierarchical 
allocation of reads improves estimation of ASE even when data are simulated from 
a non-hierarchical model. Analysis of RNA-seq data from F1 hybrid mice using 
EMASE reveals widespread ASE associated with cis-acting polymorphisms and a 
small number of parent-of-origin effects.
AVAILABILITY AND IMPLEMENTATION: EMASE software is available at 
https://github.com/churchill-lab/emase.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.