Statistical inferences for isoform expression in RNA-Seq.
H. Jiang, and W. Wong. Bioinformatics, 25 (8):
1026--1032(April 2009)assumes poisson distribution of reads; non uniformity of the read distribution is discussed later on. more complex splicing events than exon skipping also needs to be evaluated, they say..
DOI: 10.1093/bioinformatics/btp113
Abstract
SUMMARY: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods.
assumes poisson distribution of reads; non uniformity of the read distribution is discussed later on. more complex splicing events than exon skipping also needs to be evaluated, they say.
%0 Journal Article
%1 Jiang2009
%A Jiang, Hui
%A Wong, Wing Hung
%D 2009
%J Bioinformatics
%K methods chemistry;SequenceAnalysis BayesTheorem;ComputationalBiology methods;GeneExpressionProfiling;ProteinIsoforms RNA chemistry/genetics;RNA
%N 8
%P 1026--1032
%R 10.1093/bioinformatics/btp113
%T Statistical inferences for isoform expression in RNA-Seq.
%U http://dx.doi.org/10.1093/bioinformatics/btp113
%V 25
%X SUMMARY: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods.
@article{Jiang2009,
abstract = {SUMMARY: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods.},
added-at = {2010-12-31T02:55:48.000+0100},
author = {Jiang, Hui and Wong, Wing Hung},
biburl = {https://www.bibsonomy.org/bibtex/297c267def0a58ac23cc34e53807f1197/jabreftest},
doi = {10.1093/bioinformatics/btp113},
file = {Jiang2009.pdf:Jiang2009.pdf:PDF},
institution = {Institute for Computational and Mathematical Engineering and Department of Statistics, Stanford University, Stanford, CA 94305, USA.},
interhash = {5c54e78b0a82141d2801eec23106a852},
intrahash = {97c267def0a58ac23cc34e53807f1197},
journal = {Bioinformatics},
keywords = {methods chemistry;SequenceAnalysis BayesTheorem;ComputationalBiology methods;GeneExpressionProfiling;ProteinIsoforms RNA chemistry/genetics;RNA},
language = {eng},
medline-pst = {ppublish},
month = Apr,
note = {assumes poisson distribution of reads; non uniformity of the read distribution is discussed later on. more complex splicing events than exon skipping also needs to be evaluated, they say.},
number = 8,
pages = {1026--1032},
pii = {btp113},
pmid = {19244387},
timestamp = {2010-12-31T02:55:48.000+0100},
title = {Statistical inferences for isoform expression in RNA-Seq.},
url = {http://dx.doi.org/10.1093/bioinformatics/btp113},
volume = 25,
year = 2009
}