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Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been c...

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Detalles Bibliográficos
Autores principales: Wu, Hao, Xu, Tianlei, Feng, Hao, Chen, Li, Li, Ben, Yao, Bing, Qin, Zhaohui, Jin, Peng, Conneely, Karen N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666378/
https://www.ncbi.nlm.nih.gov/pubmed/26184873
http://dx.doi.org/10.1093/nar/gkv715
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author Wu, Hao
Xu, Tianlei
Feng, Hao
Chen, Li
Li, Ben
Yao, Bing
Qin, Zhaohui
Jin, Peng
Conneely, Karen N.
author_facet Wu, Hao
Xu, Tianlei
Feng, Hao
Chen, Li
Li, Ben
Yao, Bing
Qin, Zhaohui
Jin, Peng
Conneely, Karen N.
author_sort Wu, Hao
collection PubMed
description DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited. We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS.
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spelling pubmed-46663782015-12-02 Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates Wu, Hao Xu, Tianlei Feng, Hao Chen, Li Li, Ben Yao, Bing Qin, Zhaohui Jin, Peng Conneely, Karen N. Nucleic Acids Res Methods Online DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited. We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS. Oxford University Press 2015-12-02 2015-07-15 /pmc/articles/PMC4666378/ /pubmed/26184873 http://dx.doi.org/10.1093/nar/gkv715 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Wu, Hao
Xu, Tianlei
Feng, Hao
Chen, Li
Li, Ben
Yao, Bing
Qin, Zhaohui
Jin, Peng
Conneely, Karen N.
Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title_full Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title_fullStr Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title_full_unstemmed Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title_short Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
title_sort detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666378/
https://www.ncbi.nlm.nih.gov/pubmed/26184873
http://dx.doi.org/10.1093/nar/gkv715
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