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Fully Bayesian analysis of allele-specific RNA-seq data
- Source :
- Mathematical Biosciences and Engineering, Vol 16, Iss 6, Pp 7751-7770 (2019)
- Publication Year :
- 2019
- Publisher :
- AIMS Press, 2019.
-
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.
Details
- Language :
- English
- ISSN :
- 15510018
- Volume :
- 16
- Issue :
- 6
- Database :
- Directory of Open Access Journals
- Journal :
- Mathematical Biosciences and Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3aa862cb1ea843a7b4defeb6863f7329
- Document Type :
- article
- Full Text :
- https://doi.org/10.3934/mbe.2019389?viewType=HTML