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Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes
BACKGROUND: DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and pr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3535705/ https://www.ncbi.nlm.nih.gov/pubmed/23281662 http://dx.doi.org/10.1186/1471-2164-13-S8-S6 |
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author | Trimarchi, Michael P Murphy, Mark Frankhouser, David Rodriguez, Benjamin AT Curfman, John Marcucci, Guido Yan, Pearlly Bundschuh, Ralf |
author_facet | Trimarchi, Michael P Murphy, Mark Frankhouser, David Rodriguez, Benjamin AT Curfman, John Marcucci, Guido Yan, Pearlly Bundschuh, Ralf |
author_sort | Trimarchi, Michael P |
collection | PubMed |
description | BACKGROUND: DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized. METHODS: We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status. RESULTS: We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level. CONCLUSIONS: We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation. |
format | Online Article Text |
id | pubmed-3535705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35357052013-01-04 Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes Trimarchi, Michael P Murphy, Mark Frankhouser, David Rodriguez, Benjamin AT Curfman, John Marcucci, Guido Yan, Pearlly Bundschuh, Ralf BMC Genomics Research BACKGROUND: DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized. METHODS: We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status. RESULTS: We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level. CONCLUSIONS: We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation. BioMed Central 2012-12-17 /pmc/articles/PMC3535705/ /pubmed/23281662 http://dx.doi.org/10.1186/1471-2164-13-S8-S6 Text en Copyright ©2012 Trimarchi 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 | Research Trimarchi, Michael P Murphy, Mark Frankhouser, David Rodriguez, Benjamin AT Curfman, John Marcucci, Guido Yan, Pearlly Bundschuh, Ralf Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title | Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title_full | Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title_fullStr | Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title_full_unstemmed | Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title_short | Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
title_sort | enrichment-based dna methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3535705/ https://www.ncbi.nlm.nih.gov/pubmed/23281662 http://dx.doi.org/10.1186/1471-2164-13-S8-S6 |
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