S. cerevisiae RNA-Seq allele-specific expression
A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-Seq data
Overall, the data consists of:  1) one lane of RNA-Seq from the Illumina platform  2) one lane of genomic DNA sequence from the Illumina platform  3) three samples sequenced on half a flowcell from the ABI SOLiD platform. The three samples consist of one genomic DNA library and two RNA-Seq libraries (biological replicates). All three were barcoded, using four barcodes per library. The barcodes used for these samples are given below.    Name    Colorspace      Basespace  RNA1       30222           GCCTCT  RNA1       21011           GACCAC  RNA1       13330           GTATAA  RNA1       02103           GGACCG  RNA2       32031           GCTTAC  RNA2       11120           GTGTCC  RNA2       00313           GGGCAT  RNA2       23202           GATCCT  genomic DNA       01233           GGTCGC  genomic DNA      22320           GAGCTT  genomic DNA      33111           GCGTGT  genomic DNA      10002           GTTTTC
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 datasets. 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 dataset 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.