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RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.
Background: Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The a
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to bett
The analysis of differential gene expression from RNA-Seq data has become a standard for several research areas mainly involving bioinformatics. The steps for the computational analysis of these data include many data types and file formats, and a wi
The phenotypic consequences of individual mutations are modulated by the wild type genetic background in which they occur.Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist,
RNA-seq has rapidly become the de facto technique to measure gene expression. However, the time required for analysis has not kept up with the pace of data generation. Here we introduce Sailfish, a novel computational method for quantifying the abund