MethylomeDB: a database of DNA methylationprofiles of the brainYurong Xin1, Benjamin Chanrion1, Anne H. O’Donnell1,2, Maria Milekic1, Ramiro Costa1,

Yongchao Ge3 and Fatemeh G. Haghighi1,*

1Department of Psychiatry, Columbia University and The New York State Psychiatric Institute, New York,NY, 10032, 2Integrated Graduate Program in Cellular, Molecular, and Biomedical Studies, Columbia University,New York, NY, 10027 and 3Department of Neurology, Mount Sinai School of Medicine, New York, NY, 10029, USA

Received August 23, 2011; Revised October 11, 2011; Accepted November 16, 2011

ABSTRACT

MethylomeDB (http://epigenomics.columbia.edu/methylomedb/index.html) is a new databasecontaining genome-wide brain DNA methylationprofiles. DNA methylation is an important epigeneticmark in the mammalian brain. In human studies,aberrant DNA methylation alterations have beenassociated with various neurodevelopmental andneuropsychiatric disorders such as schizophrenia,and depression. In this database, we present methy-lation profiles of carefully selected non-psychiatriccontrol, schizophrenia, and depression samples. Wealso include data on one mouse forebrain sam-ple specimen to allow for cross-species compari-sons. In addition to our DNA methylation datagenerated in-house, we have and will continue toinclude published DNA methylation data fromother research groups with the focus on braindevelopment and function. Users can view themethylation data at single-CpG resolution with theoption of wiggle and microarray formats. Theycan also download methylation data for individ-ual samples. MethylomeDB offers an importantresource for research into brain function andbehavior. It provides the first source of comprehen-sive brain methylome data, encompassing whole-genome DNA methylation profiles of human andmouse brain specimens that facilitate cross-speciescomparative epigenomic investigations, as well asinvestigations of schizophrenia and depressionmethylomes.

INTRODUCTION

DNA methylation is an epigenetic modification thatoccurs at the 50-position of cytosine, altering its structure,

but not its base pairing properties. In mammaliangenomes, 5-methylcytosine occurs predominantly atCpG dinucleotides within differentiated cells, and is faith-fully propagated on the daughter strand following DNAreplication by the maintenance DNA methyltransferase 1enzyme (DNMT1). This form of information is flexibleenough to be adapted for different somatic cell types,yet stable enough to be retained during mitosis and/ormeiosis. DNA methylation is commonly associated withtranscriptional silencing because it can directly inhibit thebinding of transcription factors or regulators, or recruitmethyl-CpG binding proteins (MBPs) with repressivechromatin-remodeling functions (1,2). DNA methylationplays an important role in the protection againstintragenomic parasites (3), in genomic imprinting (4)and in X-chromosome inactivation in females.Methylation of CpG dinucleotides is critical in genomedefense and chromosomal structural integrity (3,5–7).Errors in DNA methylation establishment or mainten-ance, or environmentally mediated alterations in DNAmethylation patterns may result in phenotypicabnormalities (8). Emerging evidence have revealed thatDNA methylation alterations at selected genomic loci mayaffect social cognition (9), learning and memory (10) andstress-related behaviors (11), and contribute to aberrantgene expression in a range of neurodevelopmental dis-orders, including autism, schizophrenia, depression andAlzheimer’s disease (12–16). Although a multitude of epi-genetic marks exist, DNA methylation is the most stable, acrucial factor in studying patterns of epigenetic modifica-tions in human disease.In recent years, many new approaches have been

developed to study genome-wide DNA methylation pat-terns, providing substantial insight into the role of cyto-sine methylation in genome organization and function.Some approaches depend on the use of methylation-sensitive or -dependent restriction enzymes (17–21) wherethe level of DNA methylation is quantified by hybridiza-tion to high-density oligonucleotide arrays or sequencingvia next-generation sequencing platforms (22). Other

*To whom correspondence should be addressed. Tel: +212 543 2646; Fax: +212 543 6017; Email: fgh3@columbia.edu

Published online 2 December 2011 Nucleic Acids Research, 2012, Vol. 40, Database issue D1245–D1249doi:10.1093/nar/gkr1193

� The Author(s) 2011. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

approaches capture methylated genomic DNA usingimmunoprecipitation via an antibody that recognizes5-methylcytosine, followed by array hybridization orsequencing (23–26). Direct sequencing of bisulfite-treatedDNA allows mapping of methylation of individualcytosine nucleotides in a genome-wide fashion (27).We have developed an enzymatic-based method,Methylation Mapping Analysis by Paired-end Sequencing(Methyl-MAPS) (22) to characterize DNA methylationprofiles of primary cells from human and mousepost-mortem brain tissues. The Methyl-MAPs methoduses a battery of methylation-sensitive and -dependentendonucleases to delineate the methylation status of>80% of CpG sites genome-wide in an unbiased fashion.Fractionation by the methylation-dependent McrBC endo-nuclease generates the unmethylated compartment (22,28).The methylated compartment is generated by diges-tion with a panel of all known methylation-sensitivetetranucleotide restriction enzymes termed RE (HpaII,HhaI, AciI, BstUI and HpyCH4V). Genomic DNA fromhuman and mouse is fractionated into methylated andunmethylated compartments, and paired-end librariesare constructed, sequenced and mapped onto the respectivehuman and mouse genomes. To analyze the Methyl-MAPSdata, we have developed a data analysis pipeline referred toas Methyl-Analyzer (29) to generate methylation profiles.The focus of our research is to investigate the role

of DNA methylation in central nervous system func-tion, and to identify DNA methylation alterationsassociated with neurodevelopmental disorders, as depres-sion and schizophrenia. A genome-wide DNA methyla-tion database focusing on brain development andfunction is an invaluable resource to the community ofresearchers within the areas of neuroscience, neurobiol-ogy, psychiatry and neuro-epigenetics. In this effort, weused the Methyl-MAPS method accompanied by theMethyl-Analyzer pipeline to profile the brain methylomeof 29 human samples. Additionally, for comparative epi-genetic studies, we profiled the mouse forebrainmethylome. The methylation data in its entirety are pre-sented in a novel methylation database referred to asMethylomeDB.Although a handful of DNA methylation databases

exist in the public domain (30–32), they either containlimited methylation data or differ in biological scope.Among these methylation databases, NGSmethDB (30)collects public genome-wide methylation data generatedby next-generation sequencing approaches from variousspecies and tissue types. Our database, however, has afocused biological target that aims to characterize theneuroepigenetic landscape of both normal and abnormalhuman brain. Our study design makes it feasible toidentify potential DNA methylation signatures that maybe associated with neuropsychiatric disorders, specificallydepression and schizophrenia, using rare and well-characterized postmortem human brain specimens, withmajority of cases having complete toxicological and psy-chological autopsy data. In addition to our internallygenerated data, Methylome DB will includepublished DNA methylation data from external sources,

representing a comprehensive resource for the mammalianbrain methylome.

DATA SOURCES

Presently, MethylomeDB provides internal genome-wideDNA methylation profiles of post-mortem brain tissuesacross both human and mouse species and externalage-related DNA methylation profiles. For internal data,a total of 29 human brain specimens are represented fromthree distinct cortical regions, namely, dorsolateral pre-frontal cortex (dlPFC), ventral prefrontal cortex (vPFC)and auditory cortex (AC) (Table 1 and SupplementaryTable S1). These regions were selected because they havebeen implicated in the neuropathology of depression andschizophrenia. Within each human cortical region, bothdisease and non-psychiatric control samples have beenprofiled (matching subjects by age and sex in eachgroup). The forebrain region was profiled in a6-month-old mouse (129S6/SvEv inbred strain). Besidesthe internal human and mouse methylomes, we includedage-related DNA methylation profiles from a study con-ducted by Hernandez et al. (33), which investigates DNAmethylation changes across 90 postmortem brain samplesspanning 16–102 years in age. This large number of humansamples cover four brain regions: frontal cortex, temporalcortex, pons and cerebellum. These methylation profileswere generated by Infinium HumanMethylation27Beadchip (Illumina) which covers 27 278 CpG sites inthe human genome.

The DNA methylation data in MethylomeDB arerepresented at single-CpG resolution. We created twoMySQL databases to store the human and mousemethylomes, where each table includes data on chromo-somal mapping, 5mC chromosomal position, methylationprobability and sequence read coverage. The data analysispipeline, Methyl-Analyzer (29) estimates methylationprobabilities based on methylated (RE) and unmethylated(McrBC) digested fragments. The methylation probabilityprovides CpG methylation estimates, ranging from 0 to 1corresponding to unmethylated to methylated states. Thesamples in MethylomeDB have methylation profiles with>80% CpG genomic coverage (Supplementary Table S1).

In addition to the human and mouse methylationprofile databases, we created annotation databases. Wecompiled methylation-related CpG annotationscharacterizing CpG sites with respect to the RE orMcrBC enzyme recognition sites, and from public anno-tations we overlay associated genomic features (e.g.

Table 1. Samples used in MethylomeDB

Tissue Category Sample no. Age PMI (h)

Brain dlPFC Control 4 47±8 9.5±6.8Brain dlPFC Schizophrenia 5 41±15 10.4±4.8Brain vPFC Control 6 47±6 7.8±3.8Brain vPFC Depression 6 41±8 9.5±3.3Brain AC Control 4 47±8 7.0±2.8Brain AC Schizophrenia 4 39±17 9.0±4.2

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promoter, exon and intron features). Also, weincorporated gene, regulation, variation and conservationannotation tables from UCSC Bioinformatics GenomeBrowser.

WEB INTERFACE

We have organized the web interface of MethylomeDBinto three major functional features that include browse,search and download. The first two functions utilize thebasic interface of the UCSC Genome Browser mirror site(with the permission of UCSC Genome Bioinformaticsgroup), which we denote as the MethylomeDB Browser.Methylation data can be viewed in various formats,including (i) microarray (see representative example inFigure 1), (ii) wiggle (Figure 2), (iii) raw reads withmethylated/unmethylated fragments produced by the REand McrBC enzymatic digestions and (iv) read countreferring to sequence read coverage of CpGs in wiggleformat. Typically, the CpG methylation levels representedin microarray and wiggle formats (or tracks) will likely bethe most frequently used tracks by the user community.The wiggle format is quantitative, featuring numericalvalues, whereas the microarray format is compact,allowing for visualization of DNA methylation datafrom multiple samples at a glance. The remaining twotracks provide additional technical data specific to theMethyl-MAPS method. The fragment track representsmethylated (or unmethylated) sequence fragments thatare products of enzymatic digestions. These raw read

fragments are used in estimating CpG methylationprobabilities. The coverage track represents thecombined number of methylated and unmethylatedsequence coverage at single CpG resolution. The rawread and CpG coverage tracks together can be used togain in-depth sequencing information for each sample.These data would also be of utility to evaluate therelative accuracy or confidence associated with theestimated CpG methylation probabilities. Our analysesof biological replicates as well as validation experimentsusing an independent experimental method for methyla-tion mapping show that sequence coverage of 8� orgreater provide robust methylation estimates. Lastly, thedownload function can be accessed by the user in twoways. The Methylome Browser offers the Table featurefor easy download. Users can download whole genomeor methylation data by position for selected samples.Advanced users may want to download raw data formore sophisticated bioinformatics analyses. The‘Download’ page provides links to methylation data forall 30 samples in the current build of the database. Theseare text files with information on CpG coordinates,chromosome, methylation probabilities and coverage.

FUTURE WORK

The current version of MethylomeDB is the first release ofour database. Although it contains a wealth ofbrain-specific DNA methylation profiles in both thehuman and mouse species, the available features and

Figure 1. Methylation profiles in microarray format of 12 brain vPFC samples for gene CRHBP. Methylation probability (0–1) is encoded fromgreen (unmethylated) to black (intermediate methylation) to red (methylated) in continuous color scheme.

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functionality are still limited. However, given the import-ance of the data as a resource to the scientific community,we have made the MethylomeDB available while wecontinue to extend the database with new functionality.We plan to develop more advanced search functions

and data analysis tools for the web interface. A uniquefeature of the Methyl-MAPS method is that it allows forinterrogation of DNA methylation patterns genome-wideincluding repeat sequences that occupy the majority of thehuman genome. We are able to use the CpG annotationswe have compiled to link our DNA methylation datawith genomic features, namely, promoter, exon, intron,intergenic and repeat sequences. One useful featurewould be to search or browse the methylation distributionof user defined genomic feature(s) for any number ofsamples. Summary figures representing average methyla-tion by feature and brain region and disease state wouldbe highly appealing for users interested in, for example,

the methylation state of a specific gene promoter indifferent cortical regions or across normal and diseasesamples. Furthermore, we will create a fast track tosearch a specific genomic region or a gene. The currentversion of MethylomeDB Browser nicely displays methy-lation profiles of a position query. The fast track,however, will show average methylation across multiplesamples, and will provide downloadable data files.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online:Supplementary Table 1.

ACKNOWLEDGEMENTS

The authors would like to thank Dr Jay Gingrich formaking the mouse methylome data available for inclusion

Figure 2. Methylation profiles in wiggle format of 12 brain vPFC samples for gene CRHBP. The bars for wiggle format range from 0 to 1representing methylation probability from 0 to 1.

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in MethylomeDB and Dr Shruti Rastogi for compilingage-related DNA methylation data.

FUNDING

National Institutes of Health (NIH) (HG002915 toF.G.H.) and National Institutes of Mental Health(NIMH) (MH074118 to F.G.H.). Funding for openaccess charge: NIH (HG002915).

Conflict of interest statement. None declared.

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