title: Statistical Modeling of RNA-Seq Data.
abstract: Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest. This paper introduces a statistical model for estimating isoform abundance from RNA-Seq data and is flexible enough to accommodate both single end and paired end RNA-Seq data and sampling bias along the length of the transcript. Based on the derivation of minimal sufficient statistics for the model, a computationally feasible implementation of the maximum likelihood estimator of the model is provided. Further, it is shown that using paired end RNA-Seq provides more accurate isoform abundance estimates than single end sequencing at fixed sequencing depth. Simulation studies are also given.
authors.affiliation: Research Associate in the Department of Statistics and Biochemistry, Stanford University, Stanford, California 94305, USA.
authors.forename: Julia
authors.initials: J
authors.lastname: Salzman
authors.forename: Hui
authors.initials: H
authors.lastname: Jiang
authors.forename: Wing Hung
authors.initials: WH
authors.lastname: Wong
keywords: Fisher information; Isoform abundance estimation; Minimal sufficiency; Paired end RNA-Seq data analysis