RT Journal Article
SR Electronic
T1 Hierarchical Analysis of Multi-mapping RNA-Seq Reads Improves the Accuracy of Allele-specific Expression
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 166900
DO 10.1101/166900
A1 Narayanan Raghupathy
A1 Kwangbom Choi
A1 Matthew J. Vincent
A1 Glen L. Beane
A1 Keith Sheppard
A1 Steven C. Munger
A1 Ron Korstanje
A1 Fernando Pardo-Manual de Villena
A1 Gary A. Churchill
YR 2017
UL http://biorxiv.org/content/early/2017/07/22/166900.abstract
AB Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. Direct RNA sequencing (RNA-Seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. However, estimating ASE is challenging due to ambiguities in read alignment. Current approaches do not account for the hierarchy of multiple read alignments to genes, isoforms, and alleles. We have developed EMASE (Expectation-Maximization for Allele Specific Expression), an integrated approach to estimate total gene expression, ASE, and isoform usage based on hierarchical allocation of multi-mapping reads. In simulations, EMASE outperforms standard ASE estimation methods. We apply EMASE to RNA-Seq data from F1 hybrid mice where we observe widespread ASE associated with cis-acting polymorphisms and a small number of parent-of-origin effects at known imprinted genes. The EMASE software is freely available under GNU license at https://github.com/churchill-lab/emase and it can be adapted to other sequencing applications.