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Fully Bayesian analysis of allele-specific RNA-seq data
Mathematical Biosciences and Engineering
  • Ignacio Alvarez-Castro, Universidad de la Republica
  • Jarad Niemi, Iowa State University
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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.


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.

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Ignacio Alvarez-Castro and Jarad Niemi. "Fully Bayesian analysis of allele-specific RNA-seq data" Mathematical Biosciences and Engineering Vol. 16 Iss. 6 (2019) p. 7751 - 7770
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