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February 2011 Statistical Modeling of RNA-Seq Data
Julia Salzman, Hui Jiang, Wing Hung Wong
Statist. Sci. 26(1): 62-83 (February 2011). DOI: 10.1214/10-STS343

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.

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Julia Salzman. Hui Jiang. Wing Hung Wong. "Statistical Modeling of RNA-Seq Data." Statist. Sci. 26 (1) 62 - 83, February 2011. https://doi.org/10.1214/10-STS343

Information

Published: February 2011
First available in Project Euclid: 9 June 2011

zbMATH: 1219.62173
MathSciNet: MR2849910
Digital Object Identifier: 10.1214/10-STS343

Keywords: Fisher information , Isoform abundance estimation , minimal sufficiency , Paired end RNA-Seq data analysis

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.26 • No. 1 • February 2011
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