Cargando…

De novo identification of differentially methylated regions in the human genome

BACKGROUND: The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K arr...

Descripción completa

Detalles Bibliográficos
Autores principales: Peters, Timothy J, Buckley, Michael J, Statham, Aaron L, Pidsley, Ruth, Samaras, Katherine, V Lord, Reginald, Clark, Susan J, Molloy, Peter L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429355/
https://www.ncbi.nlm.nih.gov/pubmed/25972926
http://dx.doi.org/10.1186/1756-8935-8-6
_version_ 1782371019942526976
author Peters, Timothy J
Buckley, Michael J
Statham, Aaron L
Pidsley, Ruth
Samaras, Katherine
V Lord, Reginald
Clark, Susan J
Molloy, Peter L
author_facet Peters, Timothy J
Buckley, Michael J
Statham, Aaron L
Pidsley, Ruth
Samaras, Katherine
V Lord, Reginald
Clark, Susan J
Molloy, Peter L
author_sort Peters, Timothy J
collection PubMed
description BACKGROUND: The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. RESULTS: We show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons. CONCLUSIONS: The agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-8-6) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4429355
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-44293552015-05-14 De novo identification of differentially methylated regions in the human genome Peters, Timothy J Buckley, Michael J Statham, Aaron L Pidsley, Ruth Samaras, Katherine V Lord, Reginald Clark, Susan J Molloy, Peter L Epigenetics Chromatin Methodology BACKGROUND: The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. RESULTS: We show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons. CONCLUSIONS: The agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-8-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-01-27 /pmc/articles/PMC4429355/ /pubmed/25972926 http://dx.doi.org/10.1186/1756-8935-8-6 Text en © Peters et al.; licensee BioMed Central. 2015 This article is published under license to BioMed Central Ltd. 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 use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Peters, Timothy J
Buckley, Michael J
Statham, Aaron L
Pidsley, Ruth
Samaras, Katherine
V Lord, Reginald
Clark, Susan J
Molloy, Peter L
De novo identification of differentially methylated regions in the human genome
title De novo identification of differentially methylated regions in the human genome
title_full De novo identification of differentially methylated regions in the human genome
title_fullStr De novo identification of differentially methylated regions in the human genome
title_full_unstemmed De novo identification of differentially methylated regions in the human genome
title_short De novo identification of differentially methylated regions in the human genome
title_sort de novo identification of differentially methylated regions in the human genome
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429355/
https://www.ncbi.nlm.nih.gov/pubmed/25972926
http://dx.doi.org/10.1186/1756-8935-8-6
work_keys_str_mv AT peterstimothyj denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT buckleymichaelj denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT stathamaaronl denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT pidsleyruth denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT samaraskatherine denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT vlordreginald denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT clarksusanj denovoidentificationofdifferentiallymethylatedregionsinthehumangenome
AT molloypeterl denovoidentificationofdifferentiallymethylatedregionsinthehumangenome