  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.
