Fully Bayesian analysis of allele-specific RNA-seq data
dc.contributor.author | Alvarez-Castro, Ignacio | |
dc.contributor.author | Niemi, Jarad | |
dc.contributor.author | Niemi, Jarad | |
dc.contributor.department | Statistics | |
dc.date | 2019-09-23T15:11:52.000 | |
dc.date.accessioned | 2020-07-02T06:57:40Z | |
dc.date.available | 2020-07-02T06:57:40Z | |
dc.date.copyright | Tue Jan 01 00:00:00 UTC 2019 | |
dc.date.issued | 2019-08-23 | |
dc.description.abstract | <p>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.</p> | |
dc.description.comments | <p>This article is published as Alvarez-Castro, Ignacio, and Jarad Niemi. "Fully Bayesian analysis of allele-specific RNA-seq data." <em>Mathematical Biosciences and Engineering</em> 16 (2019): 7751-7770. doi: <a href="http://dx.doi.org/10.3934/mbe.2019389">10.3934/mbe.2019389</a>.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/stat_las_pubs/287/ | |
dc.identifier.articleid | 1289 | |
dc.identifier.contextkey | 15400672 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | stat_las_pubs/287 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/90606 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/stat_las_pubs/287/2019_Niemi_FullyBayesian.pdf|||Fri Jan 14 23:11:55 UTC 2022 | |
dc.source.uri | 10.3934/mbe.2019389 | |
dc.subject.disciplines | Computational Biology | |
dc.subject.disciplines | Genetics | |
dc.subject.disciplines | Statistical Methodology | |
dc.subject.disciplines | Statistical Models | |
dc.subject.keywords | hierarchical model | |
dc.subject.keywords | shrinkage priors | |
dc.subject.keywords | allele-specific expression | |
dc.subject.keywords | RNA-seq | |
dc.subject.keywords | Markov chain Monte Carlo | |
dc.subject.keywords | GPU | |
dc.title | Fully Bayesian analysis of allele-specific RNA-seq data | |
dc.type | article | |
dc.type.genre | article | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 31b412ec-d498-4926-901e-2cb5c2b5a31d | |
relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |
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