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.
Description
[1104.3889] Models for transcript quantification from RNA-Seq
%0 Generic
%1 pachter2011models
%A Pachter, Lior
%D 2011
%K MUSTREAD fulltext methods rna-seq
%T Models for transcript quantification from RNA-Seq
%U http://arxiv.org/abs/1104.3889
%X 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.
@misc{pachter2011models,
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.},
added-at = {2017-09-30T09:56:46.000+0200},
author = {Pachter, Lior},
biburl = {https://www.bibsonomy.org/bibtex/2603695f8e457e223b008ff7a09ff9ab9/marcsaric},
description = {[1104.3889] Models for transcript quantification from RNA-Seq},
interhash = {0e59d5cb095b8eb57f4ce2c161f3755a},
intrahash = {603695f8e457e223b008ff7a09ff9ab9},
keywords = {MUSTREAD fulltext methods rna-seq},
note = {cite arxiv:1104.3889},
timestamp = {2017-09-30T09:56:46.000+0200},
title = {Models for transcript quantification from RNA-Seq},
url = {http://arxiv.org/abs/1104.3889},
year = 2011
}