Models for transcript quantification from RNA-Seq

TL;DR

This paper reviews various models for quantifying transcript abundances from RNA-Seq data, highlighting their relationships and demonstrating that different models often produce identical estimates, with implications for downstream analyses.

Contribution

It introduces a unifying general model that encompasses many existing methods for transcript quantification from RNA-Seq data.

Findings

Many models yield identical abundance estimates despite different formulations.

A single general model captures key elements of previous methods.

Accurate abundance estimates are essential for downstream differential analysis.

Abstract

RNA-Seq is rapidly becoming the standard technology for transcriptome analysis. Fundamental to many of the applications of RNA-Seq is the quantification problem, which is the accurate measurement of relative transcript abundances from the sequenced reads. We focus on this problem, and review many recently published models that are used to estimate the relative abundances. In addition to describing the models and the different approaches to inference, we also explain how methods are related to each other. A key result is that we show how inference with many of the models results in identical estimates of relative abundances, even though model formulations can be very different. In fact, we are able to show how a single general model captures many of the elements of previously published methods. We also review the applications of RNA-Seq models to differential analysis, and explain why accurate relative transcript abundance estimates are crucial for downstream analyses.