Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression.

Authors

Narayanan Raghupathy
Kwangbom Choi, The Jackson LaboratoryFollow
Matthew J Vincent, The Jackson LaboratoryFollow
Glen L Beane, The Jackson LaboratoryFollow
Keith Sheppard, The Jackson LaboratoryFollow
Steven C. Munger, The Jackson LaboratoryFollow
Ron Korstanje, The Jackson LaboratoryFollow
Fernando Pardo-Manual de Villena
Gary Churchill, The Jackson LaboratoryFollow

Document Type

Article

Publication Date

7-1-2018

JAX Source

Bioinformatics 2018 Jul 1; 34(13):2177-2184

Volume

34

Issue

13

First Page

2177

Last Page

2184

ISSN

1367-4811

PMID

29444201

DOI

https://doi.org/10.1093/bioinformatics/bty078

Grant

GM076468

Abstract

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.

Recommended Citation

Raghupathy N, Choi K, Vincent M, Beane G, Sheppard K, Munger SC, Korstanje R, Pardo-Manual de Villena F, Churchill G. Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression. Bioinformatics 2018 Jul 1; 34(13):2177-2184