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 in | Genome Research Vol. 18; no. 5; pp. 780 - 790 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Cold Spring Harbor Laboratory Press
01.05.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1088-9051 1549-5469 1549-5469 1549-5477 |
| DOI | 10.1101/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. |
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| 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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| References | 2021111811041988000_18.5.780.31 2021111811041988000_18.5.780.10 2021111811041988000_18.5.780.32 2021111811041988000_18.5.780.30 2021111811041988000_18.5.780.24 2021111811041988000_18.5.780.25 2021111811041988000_18.5.780.22 2021111811041988000_18.5.780.23 2021111811041988000_18.5.780.28 2021111811041988000_18.5.780.29 2021111811041988000_18.5.780.26 2021111811041988000_18.5.780.27 2021111811041988000_18.5.780.9 2021111811041988000_18.5.780.8 2021111811041988000_18.5.780.7 2021111811041988000_18.5.780.6 2021111811041988000_18.5.780.5 2021111811041988000_18.5.780.3 2021111811041988000_18.5.780.20 2021111811041988000_18.5.780.2 2021111811041988000_18.5.780.21 2021111811041988000_18.5.780.1 2021111811041988000_18.5.780.13 2021111811041988000_18.5.780.35 2021111811041988000_18.5.780.14 2021111811041988000_18.5.780.11 2021111811041988000_18.5.780.33 2021111811041988000_18.5.780.12 2021111811041988000_18.5.780.34 2021111811041988000_18.5.780.17 2021111811041988000_18.5.780.18 2021111811041988000_18.5.780.15 2021111811041988000_18.5.780.16 Bird (2021111811041988000_18.5.780.4) 1987; 6 2021111811041988000_18.5.780.19 |
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| Title | Comprehensive high-throughput arrays for relative methylation (CHARM) |
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