EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific exp...

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Main Authors:	Leng, Ning, Li, Yuan, McIntosh, Brian E., Nguyen, Bao Kim, Duffin, Bret, Tian, Shulan, Thomson, James A., Dewey, Colin N., Stewart, Ron, Kendziorski, Christina
Format:	Online
Language:	English
Published:	Oxford University Press 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528625/

id	pubmed-4528625
recordtype	oai_dc
spelling	pubmed-45286252015-08-11 EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments Leng, Ning Li, Yuan McIntosh, Brian E. Nguyen, Bao Kim Duffin, Bret Tian, Shulan Thomson, James A. Dewey, Colin N. Stewart, Ron Kendziorski, Christina Original Papers Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data. Oxford University Press 2015-08-15 2015-04-05 /pmc/articles/PMC4528625/ /pubmed/25847007 http://dx.doi.org/10.1093/bioinformatics/btv193 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Leng, Ning Li, Yuan McIntosh, Brian E. Nguyen, Bao Kim Duffin, Bret Tian, Shulan Thomson, James A. Dewey, Colin N. Stewart, Ron Kendziorski, Christina
spellingShingle	Leng, Ning Li, Yuan McIntosh, Brian E. Nguyen, Bao Kim Duffin, Bret Tian, Shulan Thomson, James A. Dewey, Colin N. Stewart, Ron Kendziorski, Christina EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
author_facet	Leng, Ning Li, Yuan McIntosh, Brian E. Nguyen, Bao Kim Duffin, Bret Tian, Shulan Thomson, James A. Dewey, Colin N. Stewart, Ron Kendziorski, Christina
author_sort	Leng, Ning
title	EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
title_short	EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
title_full	EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
title_fullStr	EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
title_full_unstemmed	EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
title_sort	ebseq-hmm: a bayesian approach for identifying gene-expression changes in ordered rna-seq experiments
description	Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data.
publisher	Oxford University Press
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528625/
_version_	1613256912722198528

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

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