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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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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. |
format | Online Article Text |
id | pubmed-4666378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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