A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm...

Descripción completa

Detalles Bibliográficos
Autores principales: Cheng, Chao, Yan, Koon-Kiu, Yip, Kevin Y, Rozowsky, Joel, Alexander, Roger, Shou, Chong, Gerstein, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3188797/
https://www.ncbi.nlm.nih.gov/pubmed/21324173
http://dx.doi.org/10.1186/gb-2011-12-2-r15
_version_ 1782213413109235712
author Cheng, Chao
Yan, Koon-Kiu
Yip, Kevin Y
Rozowsky, Joel
Alexander, Roger
Shou, Chong
Gerstein, Mark
author_facet Cheng, Chao
Yan, Koon-Kiu
Yip, Kevin Y
Rozowsky, Joel
Alexander, Roger
Shou, Chong
Gerstein, Mark
author_sort Cheng, Chao
collection PubMed
description We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.
format Online
Article
Text
id pubmed-3188797
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-31887972011-10-07 A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets Cheng, Chao Yan, Koon-Kiu Yip, Kevin Y Rozowsky, Joel Alexander, Roger Shou, Chong Gerstein, Mark Genome Biol Method We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels. BioMed Central 2011 2011-02-16 /pmc/articles/PMC3188797/ /pubmed/21324173 http://dx.doi.org/10.1186/gb-2011-12-2-r15 Text en Copyright ©2011 Cheng et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Cheng, Chao
Yan, Koon-Kiu
Yip, Kevin Y
Rozowsky, Joel
Alexander, Roger
Shou, Chong
Gerstein, Mark
A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title_full A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title_fullStr A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title_full_unstemmed A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title_short A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
title_sort statistical framework for modeling gene expression using chromatin features and application to modencode datasets
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3188797/
https://www.ncbi.nlm.nih.gov/pubmed/21324173
http://dx.doi.org/10.1186/gb-2011-12-2-r15
work_keys_str_mv AT chengchao astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT yankoonkiu astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT yipkeviny astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT rozowskyjoel astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT alexanderroger astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT shouchong astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT gersteinmark astatisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT chengchao statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT yankoonkiu statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT yipkeviny statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT rozowskyjoel statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT alexanderroger statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT shouchong statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets
AT gersteinmark statisticalframeworkformodelinggeneexpressionusingchromatinfeaturesandapplicationtomodencodedatasets

Ejemplares similares