Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

8-23-2019

Journal or Book Title

Mathematical Biosciences and Engineering

Volume

16

Issue

6

First Page

7751

Last Page

7770

DOI

10.3934/mbe.2019389

Abstract

Diploid organisms have two copies of each gene, called alleles, that can be separately transcribed. The RNA abundance associated to any particular allele is known as allele-specific expression (ASE). When two alleles have polymorphisms in transcribed regions, ASE can be studied using RNA-seq read count data. ASE has characteristics different from the regular RNA-seq expression: ASE cannot be assessed for every gene, measures of ASE can be biased towards one of the alleles (reference allele), and ASE provides two measures of expression for a single gene for each biological samples with leads to additional complications for single-gene models. We present statistical methods for modeling ASE and detecting genes with differential allelic expression. We propose a hierarchical, overdispersed, count regression model to deal with ASE counts. The model accommodates gene-specific overdispersion, has an internal measure of the reference allele bias, and uses random effects to model the gene-specific regression parameters. Fully Bayesian inference is obtained using the fbseq package that implements a parallel strategy to make the computational times reasonable. Simulation and real data analysis suggest the proposed model is a practical and powerful tool for the study of differential ASE.

Comments

This article is published as Alvarez-Castro, Ignacio, and Jarad Niemi. "Fully Bayesian analysis of allele-specific RNA-seq data." Mathematical Biosciences and Engineering 16 (2019): 7751-7770. doi: 10.3934/mbe.2019389.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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