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Models for transcript quantification from RNA-Seq

Pachter, Lior (2011) Models for transcript quantification from RNA-Seq. . (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20170307-112830219

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1104.3889arXivDiscussion paper
Additional Information:Submitted on 19 Apr 2011 (v1), last revised 13 May 2011 (this version, v2). I thank Adam Roberts and Cole Trapnell for many helpful discussions that clarified our understanding of RNA-Seq and that led to many of the remarks in this paper. Meromit Singer, during the course of many discussions with me, questioned the relevance and interpretation of alternative likelihood formulations for methyl-Seq models; those conversations led to similar questions about RNA-Seq models, and finally to the comments on the equivalence between multinomial and Poisson models for RNA-Seq discussed in Section 4 and Appendix I. Colin Dewey provided many helpful suggestions and comments after reviewing a preliminary version of the manuscript. Finally, thanks to Sharon Aviran, Nicolas Bray, Ingileif Hallgrímsdόttir, Valerie Hower, Aaron Kleinman, Megan Owen, Harold Pimentel, Atif Rahman, Adam Roberts, Meromit Singer and Cole Trapnell for valuable comments and insights during the writing of this paper.
Record Number:CaltechAUTHORS:20170307-112830219
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170307-112830219
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74848
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:07 Mar 2017 19:48
Last Modified:07 Mar 2017 19:48

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