Literature DB >> 18765822

MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment.

Mattia Pelizzola1, Yasuo Koga, Alexander Eckehart Urban, Michael Krauthammer, Sherman Weissman, Ruth Halaban, Annette M Molinaro.   

Abstract

DNA methylation is an important component of epigenetic modifications that influences the transcriptional machinery and is aberrant in many human diseases. Several methods have been developed to map DNA methylation for either limited regions or genome-wide. In particular, antibodies specific for methylated CpG have been successfully applied in genome-wide studies. However, despite the relevance of the obtained results, the interpretation of antibody enrichment is not trivial. Of greatest importance, the coupling of antibody-enriched methylated fragments with microarrays generates DNA methylation estimates that are not linearly related to the true methylation level. Here, we present an experimental and analytical methodology, MEDME (modeling experimental data with MeDIP enrichment), to obtain enhanced estimates that better describe the true values of DNA methylation level throughout the genome. We propose an experimental scenario for evaluating the true relationship in a high-throughput setting and a model-based analysis to predict the absolute and relative DNA methylation levels. We successfully applied this model to evaluate DNA methylation status of normal human melanocytes compared to a melanoma cell strain. Despite the low resolution typical of methods based on immunoprecipitation, we show that model-derived estimates of DNA methylation provide relatively high correlation with measured absolute and relative levels, as validated by bisulfite genomic DNA sequencing. Importantly, the model-derived DNA methylation estimates simplify the interpretation of the results both at single-loci and at chromosome-wide levels.

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Year:  2008        PMID: 18765822      PMCID: PMC2556264          DOI: 10.1101/gr.080721.108

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  16 in total

1.  Ectopic hypermethylation of flower-specific genes in Arabidopsis.

Authors:  S E Jacobsen; H Sakai; E J Finnegan; X Cao; E M Meyerowitz
Journal:  Curr Biol       Date:  2000-02-24       Impact factor: 10.834

2.  Normalization of cDNA microarray data.

Authors:  Gordon K Smyth; Terry Speed
Journal:  Methods       Date:  2003-12       Impact factor: 3.608

Review 3.  Microarray data analysis: from disarray to consolidation and consensus.

Authors:  David B Allison; Xiangqin Cui; Grier P Page; Mahyar Sabripour
Journal:  Nat Rev Genet       Date:  2006-01       Impact factor: 53.242

4.  Rab33A: characterization, expression, and suppression by epigenetic modification.

Authors:  Elaine Cheng; Sergio E Trombetta; Daniela Kovacs; Robert D Beech; Stephan Ariyan; Miguel Reyes-Mugica; Jennifer M McNiff; Deepak Narayan; Harriet M Kluger; Mauro Picardo; Ruth Halaban
Journal:  J Invest Dermatol       Date:  2006-06-29       Impact factor: 8.551

5.  Comprehensive high-throughput arrays for relative methylation (CHARM).

Authors:  Rafael A Irizarry; Christine Ladd-Acosta; Benilton Carvalho; Hao Wu; Sheri A Brandenburg; Jeffrey A Jeddeloh; Bo Wen; Andrew P Feinberg
Journal:  Genome Res       Date:  2008-03-03       Impact factor: 9.043

6.  A 5' 2-kilobase-pair region of the imprinted mouse H19 gene exhibits exclusive paternal methylation throughout development.

Authors:  K D Tremblay; K L Duran; M S Bartolomei
Journal:  Mol Cell Biol       Date:  1997-08       Impact factor: 4.272

7.  Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells.

Authors:  Michael Weber; Jonathan J Davies; David Wittig; Edward J Oakeley; Michael Haase; Wan L Lam; Dirk Schübeler
Journal:  Nat Genet       Date:  2005-07-10       Impact factor: 38.330

8.  Methyl-CpG binding proteins identify novel sites of epigenetic inactivation in human cancer.

Authors:  Esteban Ballestar; Maria F Paz; Laura Valle; Susan Wei; Mario F Fraga; Jesus Espada; Juan Cruz Cigudosa; Tim Hui-Ming Huang; Manel Esteller
Journal:  EMBO J       Date:  2003-12-01       Impact factor: 11.598

9.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

10.  Microarray-based DNA methylation profiling: technology and applications.

Authors:  Axel Schumacher; Philipp Kapranov; Zachary Kaminsky; James Flanagan; Abbas Assadzadeh; Patrick Yau; Carl Virtanen; Neil Winegarden; Jill Cheng; Thomas Gingeras; Arturas Petronis
Journal:  Nucleic Acids Res       Date:  2006-01-20       Impact factor: 16.971

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  53 in total

Review 1.  Statistical approaches for the analysis of DNA methylation microarray data.

Authors:  Kimberly D Siegmund
Journal:  Hum Genet       Date:  2011-04-26       Impact factor: 4.132

Review 2.  DNA methylation: an epigenetic risk factor in preterm birth.

Authors:  Ramkumar Menon; Karen N Conneely; Alicia K Smith
Journal:  Reprod Sci       Date:  2012-01       Impact factor: 3.060

3.  Masculine epigenetic sex marks of the CYP19A1/aromatase promoter in genetically male chicken embryonic gonads are resistant to estrogen-induced phenotypic sex conversion.

Authors:  Haley L Ellis; Keiko Shioda; Noël F Rosenthal; Kathryn R Coser; Toshi Shioda
Journal:  Biol Reprod       Date:  2012-07-26       Impact factor: 4.285

4.  A semantic web framework to integrate cancer omics data with biological knowledge.

Authors:  Matthew E Holford; James P McCusker; Kei-Hoi Cheung; Michael Krauthammer
Journal:  BMC Bioinformatics       Date:  2012-01-25       Impact factor: 3.169

5.  Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage.

Authors:  Lukas Chavez; Justyna Jozefczuk; Christina Grimm; Jörn Dietrich; Bernd Timmermann; Hans Lehrach; Ralf Herwig; James Adjaye
Journal:  Genome Res       Date:  2010-08-27       Impact factor: 9.043

6.  Accurate genome-scale percentage DNA methylation estimates from microarray data.

Authors:  Martin J Aryee; Zhijin Wu; Christine Ladd-Acosta; Brian Herb; Andrew P Feinberg; Srinivasan Yegnasubramanian; Rafael A Irizarry
Journal:  Biostatistics       Date:  2010-09-21       Impact factor: 5.899

Review 7.  Methods in DNA methylation profiling.

Authors:  Tao Zuo; Benjamin Tycko; Ta-Ming Liu; Juey-Jen L Lin; Tim H-M Huang
Journal:  Epigenomics       Date:  2009-12       Impact factor: 4.778

8.  Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation.

Authors:  Mark D Robinson; Clare Stirzaker; Aaron L Statham; Marcel W Coolen; Jenny Z Song; Shalima S Nair; Dario Strbenac; Terence P Speed; Susan J Clark
Journal:  Genome Res       Date:  2010-11-02       Impact factor: 9.043

9.  Epigenetic approaches for the detection of fetal DNA in maternal plasma.

Authors:  Dana Wy Tsui; Rossa Wk Chiu; Ym Dennis Lo
Journal:  Chimerism       Date:  2010 Jul-Sep

10.  MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case--control samples.

Authors:  Karolina A Aberg; Joseph L McClay; Srilaxmi Nerella; Lin Y Xie; Shaunna L Clark; Alexandra D Hudson; Jozsef Bukszár; Daniel Adkins; Christina M Hultman; Patrick F Sullivan; Patrik K E Magnusson; Edwin J C G van den Oord
Journal:  Epigenomics       Date:  2012-12       Impact factor: 4.778

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