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.

HumanDNA methylation data stored in NCBI (GEO) Dataset GSM2819624; liver tissue sample 7012_ CV_ RRBS in SEEK [https://seek.lisym.org/samples/134]

Supplementary_files_format_and_content: RRBS: bed files contain genomic coordinates, methylation values and coverage of all covered CpGs of a sample; bigwig files (.CG.bw) contain methylation values and bigwig files (coverage_ct.bw) coverage information.

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.

Human disease methylation database. DiseaseMeth version 2.0 is focused on aberrant methylomes of human diseases. Used for understanding of DNA methylation driven human diseases.

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.

HumanDNA methylation data set GSM2819671 stored in NCBI (GEO)

Epigenetic changes in the DNA methylome are increasingly shown to play an integral role in regulating gene expression necessary for plants’ adaption to environmental stressors. Plants subjected to the novel environment of spaceflight onboard the International Space Station (ISS), show stress-related transcriptomic changes most notably associated with pathogen stress response. Here, we investigate how known terrestrial stress associated epigenetic modulations might play a role in spaceflight adaptation. To examine the role of 5mCyt in spaceflight adaptation, the APEX04-EPEX experiment conducted onboard the ISS evaluated the spaceflight altered genome wide methylation profiles of two methylation regulating gene mutants, methyltransferase 1 (met1-7) and elongator complex subunit 2 (elp2-5), that are involved in pathogen defense response, along with a wild type Col-0 control. MethylSeq and RNAseq analyses were performed on both spaceflight grown samples and ground grown controls. In addition, the epigenetics effects that may contribute to the differential gene expression patterns observed between leaf and root tissues were also investigated in an organ-specific manner.

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.

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

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/.

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.

Bulk methylation array data from 32 prostate tissue samples from 8 patients (3 with post-surgery relapse). Methylation data were acquired using the microarray assay Illumina Infinium MethylationEPIC v2.0 Kit. The individual samples have information on patient origin and sample type (cancer, cancer-adjacent field-effect normal or normal sample far from cancer). Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

Summary of human DNA methylation data available on GEO listed by sequencing technology on 01/03/2019.

Patient cohort details of the main DNA methylation data-sets analysed.

Summary and Overall design from the GEO: "DNA methylation classification reference set (1077) and validation set (428) of 1505 sarcoma samples using Illumina HumanMethylation450 BeadChips or Illumina Infinium HumanMethylation850 BeadChips"

This data set is used to support the classification of soft tissue and bone tumors using a machine learning classifier algorithm based on array-generated DNA methylation data. The tool created is available at: www.molecularsarcomapathology.org.

Abstract from the study:
"Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications."

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.

Comprehensive DNA methylation database. Provides genome wide mapping of methylation sites to target genes through integrating a large number of DNA methylation and gene expression profiles in human diseases, and to annotate methylation sites to abundant regulatory elements and TFs.

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

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/.

Raw data used in this research. Abbreviations: case identifier (ID); standardized PTSD CheckList (stPCL); PCL (PTSD CheckList); standardized methylation (StMethyl); traumatic brain injury (TBI), Standardized hippocampal volume (StHip); standardized education (StEdu); standardized right hippocampal volume (StRHip); standardized left hippocampal volume (StLHip); standardized re-experiencing (StReex); standardized avoidance (stAvoid); standardized arousal (StArous). (TXT)