Statistical Modeling of RNA-Seq Data

TL;DR

This paper presents a statistical model for estimating isoform abundance from RNA-Seq data, accommodating bias and data type, with evidence that paired end sequencing yields more accurate results than single end.

Contribution

It introduces a flexible statistical model for RNA-Seq data analysis, including a maximum likelihood estimator and insights into data type advantages.

Findings

Paired end RNA-Seq provides more accurate isoform estimates than single end.

The model accounts for sampling bias along transcript length.

Simulation studies validate the model's effectiveness.

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.