A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Accurately quantifying biological age is crucial for understanding the mechanisms of aging and developing effective interventions. Molecular aging clocks, particularly epigenetic clocks that use DNA methylation data to estimate biological age, have become essential tools in this area of research. However, the lack of a comprehensive, publicly accessible database with uniformly formatted DNA methylation datasets across various ages and tissues complicates the investigation of epigenetic clocks. Researchers face significant challenges in locating relevant datasets, accessing key information from raw data, and managing inconsistent data formats and metadata annotations. Additionally, there is a lack of dedicated resources for aging-related differentially methylated sites (DMSs, also named differentially methylated positions or differentially methylated cytosines) and regions (DMRs), which hinders progress in understanding the epigenetic mechanisms of aging. To address these challenges, we developed MethAgingDB, a comprehensive DNA methylation database for aging biology. MethAgingDB includes 93 datasets, with 11474 profiles from 13 distinct human tissues and 1361 profiles from 9 distinct mouse tissues. The database provides preprocessed DNA methylation data in a consistent matrix format, along with tissue-specific DMSs and DMRs, gene-centric aging insights, and an extensive collection of epigenetic clocks. Together, MethAgingDB is expected to streamline aging-related epigenetic research and support the development of robust, biologically informed aging biomarkers.

DNA methylation, one of the most important epigenetic modifications, plays a crucial role in various biological processes. The level of DNA methylation can be measured using whole-genome bisulfite sequencing at single base resolution. However, until now, there is a paucity of publicly available software for carrying out integrated methylation data analysis. In this study, we implemented Methy-Pipe, which not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, it uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages. To demonstrate its utility and performance, we applied it to both real and simulated bisulfite sequencing datasets. The results indicate that Methy-Pipe can accurately estimate methylation densities, identify DMRs and provide a variety of utility programs for downstream methylation data analysis. In summary, Methy-Pipe is a useful pipeline that can process whole genome bisulfite sequencing data in an efficient, accurate, and user-friendly manner. Software and test dataset are available at http://sunlab.lihs.cuhk.edu.hk/methy-pipe/.

Human DNA methylation data stored in NCBI (GEO) Dataset GSM281962; liver tissue sample 7041_CV_RRBS https://seek.lisym.org/samples/135

Motivation Bisulfite sequencing data carry invaluable information about epigenetic states of a cell population beyond DNA methylation levels. Phased DNA methylation states (DNA methylation pattern; i.e., an array of DNA methylation states of CpGs simultaneously covered by a single read) can serve as a local barcode representing the epigenetic state of a single cell. Therefore we can compute approximate epigenetic diversity through measuring the diversity of DNA methylation patterns (inter-molecule / inter-cellular heterogeneity). On the other hand, DNA methylation patterns also inform us of the local disorder of DNA methylation states, which already have been shown to have prognostic potential (Landau et al., 2014). To facilitate studies on such concept of DNA methylation heterogeneity, we developed an efficient software named Metheor and here provide a comprehensive DNA methylation profiles of 928 cancer cell lines from cancer cell line encyclopedia (CCLE) computed by Metheor. Data processing Raw reduced representation bisulfite sequencing (RRBS) reads for 928 CCLE cell lines were downloaded under SRA study accession SRP186687, and preprocessed using Trim Galore! v0.6.7 with --rrbs option. Reads were then aligned to hg38 reference genome using Bismark v0.23.1. The resulting alignments are used to compute DNA methylation heterogeneity levels (see below) through Metheor v0.1.0. Seven measures for DNA methylation heterogeneity Profiles of seven DNA methylation heterogeneity measures are provided in this dataset.

Proportion of discordant reads (PDR) Local pairwise methylation disorder (LPMD) Methylation haplotype load (MHL) Epipolymorphism (PM) Methylation entropy (ME) Fraction of discordant read pairs (FDRP) Quantitative fraction of discordant pairs (qFDRP)

For a more detailed description of those measures, please refer to this GitHub repository. Data tables We provide 7 tables for DNA methylation heterogeneity profiles and an additional table that contains the average methylation level information.

ccle.pdr.csv: Table for average proportion of discordant reads (PDR) for various genomic contexts ccle.lpmd.csv:Table for average local pairwise methylation disorder (LPMD) for various genomic contexts ccle.mhl.csv: Table for average methylation haplotype load (MHL) for various genomic contexts ccle.pm.csv: Table for average epipolymorphism (PM) for various genomic contexts ccle.me.csv: Table for average methylation entropy (ME) for various genomic contexts. ccle.fdrp.csv: Table for average FDRP levels for various genomic contexts. ccle.qfdrp.csv: Table for average qFDRP levels for various genomic contexts. ccle.beta.csv: Table for average DNA methylation levels for various genomic contexts.

Schema for data tables All data tables are in comma-separated values (csv) format sharing the following columns:

cell_line_name: Identifier for the cell line. run_accession: SRA run accession of the corresponding RRBS data. tissue: Tissue collection site. disease: Full disease type (e.g., carcinoma (ductal carcinoma), carcinoma (squamous_cell_carcinoma), or lymphoid_noeplasm (Hodgkin_lymphoma)) disease_primary: General disease type (e.g., carcinoma or lymphoid_neoplasm). disease_secondary: Specific disease type (e.g., ductal carcinoma, squamous_cell_carcinoma or Hodgkin_lymphoma). disease_stage: Indicates whether tissue sample is from primary or metastatic site. age_at_sampling: Age of tissue donor at sampling if known. Otherwise, values are left empty. sex: Sex of tissue donor if known. Otherwise, values are left empty. ethnicity: Ethnicity of tissue donor if known. Otherwise, values are left empty. genomewide: Genomewide average DNA methylation heterogeneity levels. promoter: Average DNA methylation heterogeneity levels at promoters of protein-coding genes. cgi: Average DNA methylation heterogeneity levels at CpG islands. Annotations were downloaded from UCSC table browser. cpg_shore: Average DNA methylation heterogeneity levels at CpG shores. CpG shores are defined as 2kb regions flanking upstream or downstream of CpG islands. Regions overlapping CpG islands were excluded. cpg_shelf: Average DNA methylation heterogeneity levels at CpG shelves. CpG shelves are defined as 2kb regions flanking upstream or downstream of (CpG island + CpG shore) regions. Regions overlapping CpG islands or shores were excluded. methylation_canyon: Average DNA methylation heterogeneity levels at methylation canyons. DNA methylation canyons are defined as broad (> 3.5kb) under-methylated regions (Jeong et al., 2014), and their hg38 annotations were downloaded from (Su et al., 2018). exon: Average DNA methylation heterogeneity levels at exons of protein coding genes. intron: Average DNA methylation heterogeneity levels at introns of protein coding genes. gene_body: Average DNA methylation heterogeneity levels at gene bodies of protein coding genes. LINE: Average DNA methylation heterogeneity levels at LINEs. Annotations were downloaded from UCSC table browser (hg38, Repeats-RepeatMasker). SINE: Average DNA methylation heterogeneity levels at SINEs LTR: Average DNA methylation heterogeneity levels at LTR retrotransposons

Availability of Metheor The source code for Metheor can be found at https://github.com/dohlee/metheor You can install Metheor using conda at commandline: $ conda install -c dohlee metheor

Protein arginine (R) methylation is a post-translational modification that has been shown to play a role in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction and tumor development. Here, we present a dataset of high-quality methylations obtained from several different heavy methyl SILAC (hmSILAC) experiments analyzed with a machine learning model that was trained to recognize hmSILAC doublets and show that this model allows for improved high-confidence identification of methyl-peptides. The results of our analysis of the interactions between R-methylated proteins further support the idea that this modification plays a role in modulating protein:protein interactions and suggest a potential new role of R methylation in immunity and macrophage metabolism. Moreover, we intersect the methyl-site dataset with a phosphosite dataset to investigate the cross-talk between R methylation and phosphorylation. Finally, we explore the application of hmSILAC to identify unconventional methylated residues on both histone and non-histone proteins.

Database to study interplay of DNA methylation, gene expression and cancer that hosts both highly integrated data of DNA methylation, cancer-related gene, mutation and cancer information from public resources, and the CpG Island (CGI) clones derived from our large-scale sequencing. Interconnections between different data types were analyzed and presented. Search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. The search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. As part of the Cancer Epigenomics Project in China, MethyCancer serves as a platform for sharing data and analytical results from the Cancer Genome/Epigenome Project in China with colleagues all over the world.

Maternal obesity alters placental tissue function and morphology with a corresponding increase in local inflammation.; We and others showed that placenta size, inflammation and fetal growth are regulated by maternal diet and obesity status. Maternal obesity alters placental DNA methylation which in turn could likely impact gene transcription of; of proteins critical for normal fetal development. RNA-binding motif single-stranded interacting protein 1 (RBMS1) is expressed by the placenta and likely modulates DNA replication and transcription regulation.; Serum RBMS1 protein concentration is increased with maternal obesity and RBMS1 gene expression in liver tissue is induced by a high-fat diet and inflammation. However, it is not yet known whether placental RBMS1 mRNA expression and DNA methylation are altered by maternal obesity.

Human DNA methylation data set GSM2819638 stored in NCBI (GEO)

This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.

Differential methylation data of human cataracts in the GEO database (GSE241767).

Purpose: In a significant proportion of children born small-for-gestational age (SGA) with failure of catch-up growth, the etiology of short stature remains unclear after routine diagnostic work-up. We wanted to investigate if extensive analysis of the (epi)genome can unravel the cause of growth failure in a significant portion of these children. Patients and Methods: Twenty SGA children treated with growth hormone (GH) because of short stature were selected from the BELGROW database of the Belgian Society for Pediatric Endocrinology and Diabetology for exome sequencing, SNP array and genome-wide methylation analysis to identify the (epi)genetic cause. First year response to GH was compared to the response of SGA patients in the KIGS database. Results: We identified (likely) pathogenic variants in 4 children (from 3 families) using exome sequencing and found pathogenic CNV in 2 probands using SNP array. In a child harboring a NSD1-containing microduplication, we identified a DNA methylation signature that is opposite to the genome-wide DNA methylation signature of Sotos syndrome. Moreover, we observed multi-locus imprinting disturbances in two children in whom no other genomic alteration could be identified. Five out of 6 children with a genetic diagnosis had an "above average" response to GH. Conclusions: The study indicates that a more advanced approach with deep genotyping can unravel unexpected (epi)genomic alterations in SGA children with persistent growth failure. Most SGA children with a genetic diagnosis had a good response to GH treatment.

Top P-Values for OXPHOS CpGs showing differential methylation by reproductive period (Age Menopause–Age Menarche).

Background: Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk.

Results: We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs, (e.g. through disruption of transcription factor binding sites for CTCF and SMC3), and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites).

Conclusions: Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk/.

A dedicated database for the storage, browsing and data mining of whole-genome, single-base-pair resolution methylomes.

Genome-wide profiling of DNA methylation levels by RRBS in 349 samples, derived from 112 glioblastoma (IDH wildtype) patients, 13 IDH muated brain tumor patients, and 5 normal brain controls. For each patient samples from at least two and up to six tumor resections are available. For 6 patients multiple regions of each tumor were sampled.

Genome-wide profiling of DNA methylation levels by RRBS in 150 glioblastoma tumor samples. Patients were selected to represent the general population of glioblastoma patients based on Austrian Brain Tumor Registry. These DNA methylation profiles were created for the validation of the glioblastoma progression study (GBMatch) and consist of 106 profiles from FFPE samples and 44 profiles from fresh-frozen samples. For the 44 fresh-frozen samples also WGS data (43 genomes) and RNA-seq data (37 transcriptomes) have been produced for validation purposes.

This dataset contains data for adult male fathead minnows exposed to EE2 (a synthetic estrogen). It is a targeted study on the response of the esr1 gene (expression and DNA methylation profiles) to estrogen exposure. It contains mean and standard deviation for DNA methylation levels per treatment group, and gene expression data.

This data contains DNA methylation data obtained from the PBMCs obtained from type 2 diabetes adolescents and controls. There are 21 diabetic samples and 10 controls. This dataset also contains metabolic data obtained from the serum of 155 samples. There are 113 diabetic and 42 control samples.

Raw idat files for DNA methylation profiling for 12 CCAs and 7 normal bile duct tissues. DNA methylation profiling was performed using Infinium MethylationEPIC v2.0 Kit.