@article{Skelly01102011,
author = {Skelly, Daniel A. and Johansson, Marnie and Madeoy, Jennifer and Wakefield, Jon and Akey, Joshua M.}, 
title = {A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data},
volume = {21}, 
number = {10}, 
pages = {1728-1737}, 
year = {2011}, 
doi = {10.1101/gr.119784.110}, 
abstract ={Variation in gene expression is thought to make a significant contribution to phenotypic diversity among individuals within populations. Although high-throughput cDNA sequencing offers a unique opportunity to delineate the genome-wide architecture of regulatory variation, new statistical methods need to be developed to capitalize on the wealth of information contained in RNA-seq data sets. To this end, we developed a powerful and flexible hierarchical Bayesian model that combines information across loci to allow both global and locus-specific inferences about allele-specific expression (ASE). We applied our methodology to a large RNA-seq data set obtained in a diploid hybrid of two diverse Saccharomyces cerevisiae strains, as well as to RNA-seq data from an individual human genome. Our statistical framework accurately quantifies levels of ASE with specified false-discovery rates, achieving high reproducibility between independent sequencing platforms. We pinpoint loci that show unusual and biologically interesting patterns of ASE, including allele-specific alternative splicing and transcription termination sites. Our methodology provides a rigorous, quantitative, and high-resolution tool for profiling ASE across whole genomes.}, 
URL = {http://genome.cshlp.org/content/21/10/1728.abstract}, 
eprint = {http://genome.cshlp.org/content/21/10/1728.full.pdf+html}, 
journal = {Genome Research} 
}