Comprehensive high-throughput arrays for relative methylation (CHARM)

This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fr...

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Published inGenome Research Vol. 18; no. 5; pp. 780 - 790
Main Authors Irizarry, Rafael A., Ladd-Acosta, Christine, Carvalho, Benilton, Wu, Hao, Brandenburg, Sheri A., Jeddeloh, Jeffrey A., Wen, Bo, Feinberg, Andrew P.
Format Journal Article
LanguageEnglish
Published United States Cold Spring Harbor Laboratory Press 01.05.2008
Subjects
Online AccessGet full text
ISSN1088-9051
1549-5469
1549-5469
1549-5477
DOI10.1101/gr.7301508

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Abstract This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., ∼100% sensitivity at 90% specificity with McrBC. We term this approach “comprehensive high-throughput arrays for relative methylation” (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
AbstractList This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., approximately 100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., approximately 100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., ∼100% sensitivity at 90% specificity with McrBC. We term this approach “comprehensive high-throughput arrays for relative methylation” (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., approximately 100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g., similar to 100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
Author Wu, Hao
Jeddeloh, Jeffrey A.
Irizarry, Rafael A.
Ladd-Acosta, Christine
Carvalho, Benilton
Feinberg, Andrew P.
Wen, Bo
Brandenburg, Sheri A.
AuthorAffiliation 2 Department of Medicine and Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
3 Orion Genomics, LLC, St. Louis, Missouri 63108, USA
1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
AuthorAffiliation_xml – name: 2 Department of Medicine and Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
– name: 3 Orion Genomics, LLC, St. Louis, Missouri 63108, USA
– name: 1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
Author_xml – sequence: 1
  givenname: Rafael A.
  surname: Irizarry
  fullname: Irizarry, Rafael A.
– sequence: 2
  givenname: Christine
  surname: Ladd-Acosta
  fullname: Ladd-Acosta, Christine
– sequence: 3
  givenname: Benilton
  surname: Carvalho
  fullname: Carvalho, Benilton
– sequence: 4
  givenname: Hao
  surname: Wu
  fullname: Wu, Hao
– sequence: 5
  givenname: Sheri A.
  surname: Brandenburg
  fullname: Brandenburg, Sheri A.
– sequence: 6
  givenname: Jeffrey A.
  surname: Jeddeloh
  fullname: Jeddeloh, Jeffrey A.
– sequence: 7
  givenname: Bo
  surname: Wen
  fullname: Wen, Bo
– sequence: 8
  givenname: Andrew P.
  surname: Feinberg
  fullname: Feinberg, Andrew P.
BackLink https://www.ncbi.nlm.nih.gov/pubmed/18316654$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1038/ng1598
10.1093/hmg/ddl095
10.1007/s10577-005-1015-4
10.1038/416552a
10.1158/1535-7163.MCT-06-0609
10.1093/carcin/bgl161
10.1093/nar/28.8.e32
10.1093/nar/30.4.e15
10.1093/bioinformatics/btk046
10.1038/nature02651
10.1016/j.ab.2004.05.007
10.1038/83825
10.1371/journal.pbio.0020405
10.1038/nrg1749
10.2307/2986349
10.1016/j.cell.2007.01.029
10.1101/gr.202801
10.1038/sj.bjc.6602918
10.1038/ng1909
10.1093/bioinformatics/19.2.185
10.1016/0022-2836(92)90925-A
10.1038/nature06008
10.1101/gr.4410706
10.1002/gcc.20243
10.1089/cmb.2005.12.882
10.1016/j.ccr.2004.06.010
10.1093/nar/25.12.2532
10.1007/s10577-005-1507-2
10.1002/j.1460-2075.1987.tb04851.x
10.1086/524110
10.1101/gr.161800
10.1101/gr.5273806
10.1002/jcb.20146
10.1093/biostatistics/2.2.183
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  doi: 10.1038/ng1598
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  doi: 10.1093/hmg/ddl095
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  doi: 10.1007/s10577-005-1015-4
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  doi: 10.1038/416552a
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  doi: 10.1158/1535-7163.MCT-06-0609
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  doi: 10.1093/carcin/bgl161
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  doi: 10.1093/nar/28.8.e32
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  doi: 10.1371/journal.pbio.0020405
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  doi: 10.1038/nrg1749
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  doi: 10.2307/2986349
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  doi: 10.1016/j.cell.2007.01.029
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  doi: 10.1101/gr.202801
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  doi: 10.1038/sj.bjc.6602918
– ident: 2021111811041988000_18.5.780.31
– ident: 2021111811041988000_18.5.780.9
  doi: 10.1038/ng1909
– ident: 2021111811041988000_18.5.780.5
  doi: 10.1093/bioinformatics/19.2.185
– ident: 2021111811041988000_18.5.780.30
  doi: 10.1016/0022-2836(92)90925-A
– ident: 2021111811041988000_18.5.780.22
  doi: 10.1038/nature06008
– ident: 2021111811041988000_18.5.780.3
  doi: 10.1101/gr.4410706
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  doi: 10.1002/gcc.20243
– ident: 2021111811041988000_18.5.780.33
  doi: 10.1089/cmb.2005.12.882
– ident: 2021111811041988000_18.5.780.1
  doi: 10.1016/j.ccr.2004.06.010
– ident: 2021111811041988000_18.5.780.34
  doi: 10.1093/nar/25.12.2532
– ident: 2021111811041988000_18.5.780.21
  doi: 10.1007/s10577-005-1507-2
– volume: 6
  start-page: 999
  year: 1987
  ident: 2021111811041988000_18.5.780.4
  article-title: Non-methylated CpG-rich islands at the human alpha-globin locus: Implications for evolution of the alpha-globin pseudogene
  publication-title: EMBO J.
  doi: 10.1002/j.1460-2075.1987.tb04851.x
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  doi: 10.1086/524110
– ident: 2021111811041988000_18.5.780.23
  doi: 10.1101/gr.161800
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  doi: 10.1101/gr.5273806
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  doi: 10.1002/jcb.20146
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  doi: 10.1093/biostatistics/2.2.183
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Snippet This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation...
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SubjectTerms Bias
CpG Islands - genetics
DNA Methylation
Genome, Human - genetics
Genomics
Humans
Methods
Oligonucleotide Array Sequence Analysis - methods
Reference Standards
Reproducibility of Results
Sensitivity and Specificity
Title Comprehensive high-throughput arrays for relative methylation (CHARM)
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