Cargando…

Quantitative comparison of within-sample heterogeneity scores for DNA methylation data

DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell po...

Descripción completa

Detalles Bibliográficos
Autores principales: Scherer, Michael, Nebel, Almut, Franke, Andre, Walter, Jörn, Lengauer, Thomas, Bock, Christoph, Müller, Fabian, List, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7192612/
https://www.ncbi.nlm.nih.gov/pubmed/32103242
http://dx.doi.org/10.1093/nar/gkaa120
_version_ 1783528041801056256
author Scherer, Michael
Nebel, Almut
Franke, Andre
Walter, Jörn
Lengauer, Thomas
Bock, Christoph
Müller, Fabian
List, Markus
author_facet Scherer, Michael
Nebel, Almut
Franke, Andre
Walter, Jörn
Lengauer, Thomas
Bock, Christoph
Müller, Fabian
List, Markus
author_sort Scherer, Michael
collection PubMed
description DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.
format Online
Article
Text
id pubmed-7192612
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-71926122020-05-06 Quantitative comparison of within-sample heterogeneity scores for DNA methylation data Scherer, Michael Nebel, Almut Franke, Andre Walter, Jörn Lengauer, Thomas Bock, Christoph Müller, Fabian List, Markus Nucleic Acids Res Methods Online DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows. Oxford University Press 2020-05-07 2020-02-27 /pmc/articles/PMC7192612/ /pubmed/32103242 http://dx.doi.org/10.1093/nar/gkaa120 Text en © The Author(s) 2020. 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
Scherer, Michael
Nebel, Almut
Franke, Andre
Walter, Jörn
Lengauer, Thomas
Bock, Christoph
Müller, Fabian
List, Markus
Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title_full Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title_fullStr Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title_full_unstemmed Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title_short Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
title_sort quantitative comparison of within-sample heterogeneity scores for dna methylation data
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7192612/
https://www.ncbi.nlm.nih.gov/pubmed/32103242
http://dx.doi.org/10.1093/nar/gkaa120
work_keys_str_mv AT scherermichael quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT nebelalmut quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT frankeandre quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT walterjorn quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT lengauerthomas quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT bockchristoph quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT mullerfabian quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata
AT listmarkus quantitativecomparisonofwithinsampleheterogeneityscoresfordnamethylationdata