a120735fd0e3914968ffd26170946de5ab960135 gperez2 Sat Mar 7 16:28:02 2026 -0800 Reorganizing trackDb, updating name for files, updating shortLabels, updating the Human Methylation Atlas Summary and Human Methylation Atlas Signals track description pages, refs #36826 diff --git src/hg/makeDb/trackDb/human/methylationAtlas.html src/hg/makeDb/trackDb/human/methylationAtlas.html new file mode 100755 index 00000000000..de38a0ac535 --- /dev/null +++ src/hg/makeDb/trackDb/human/methylationAtlas.html @@ -0,0 +1,175 @@ +

Description

+

+The Human Methylation Atlas tracks display genome-wide DNA methylation profiles from +deep whole-genome bisulfite sequencing (WGBS) of 39 primary human cell types +sorted from 205 healthy tissue samples. This comprehensive resource enables fragment-level +analysis across thousands of unique markers, providing a detailed reference for +cell-type-specific methylation patterns. +

+ +Human Methylation Atlas Summary consists of the following subtracks: + +

+Unsupervised clustering of these methylomes recapitulates key elements of tissue ontogeny and +developmental lineage relationships. +

+ +

Display Conventions and Configuration

+ +

Track Colors

+

+Tracks are colored by tissue/cell type category as follows: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ColorCell Type(s)
 Neurons
 Oligodendrocytes
 Thyroid Epithelium
 Prostate Epithelium
 Bladder Epithelium
 Heart Cardiomyocytes
 Smooth Muscle
 Heart Fibroblasts
 Skeletal Muscle
 Erythrocyte Progenitors
 Blood Granulocytes
 Blood Monocytes/Macrophages
 Blood T Cells
 Blood B Cells
 Blood NK Cells
 Pancreas Beta Cells
 Pancreas Alpha Cells
 Pancreas Delta Cells
 Pancreas Duct Cells
 Pancreas Acinar Cells
 Colon Epithelium
 Colon Fibroblasts
 Small Intestine Epithelium
 Gastric Epithelium
 Gallbladder
 Liver Hepatocytes
 Lung Bronchus Epithelium
 Lung Alveolar Epithelium
 Kidney Epithelium
 Endothelial
 Breast Basal Epithelium
 Breast Luminal Epithelium
 Fallopian Epithelium
 Ovary Epithelium
 Adipocytes
 Epidermal Keratinocytes
 Dermal Fibroblasts
 Bone Osteoblasts
 Head Neck Epithelium
+ +

Methods

+ +

Sample Collection and Sequencing

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+Primary human cells were isolated from freshly dissociated adult healthy tissues using +fluorescence-activated cell sorting (FACS), yielding high-purity preparations across major +cell lineages. A total of 205 samples representing 77 primary cell types were collected from +137 consenting donors and merged into 39 cell type groups based on methylation similarity. +Average sample purity exceeded 90% as determined by flow cytometry, gene expression, and +DNA methylation analysis. Some cell types showed lower purity, including colon fibroblasts (78%), +smooth muscle cells (82%), endothelial cells (86%), and adipocytes (87%). +

+ +

+Several cell types are absent from the atlas, typically due to limited availability of primary +material. These include osteoblasts, cholangiocytes, cells of the adrenal gland, urethral +epithelium, and haematopoietic stem cells. Subpopulations of interest, such as distinct neuronal or +lymphocyte subtypes, were also not resolved separately. +

+ +

+Whole-genome bisulfite sequencing was performed using 150 bp paired-end reads at an average +sequencing depth of 30× (minimum 6.62×). Libraries were prepared using the +Accel-NGS Methyl-Seq DNA library preparation kit and sequenced on the Illumina NovaSeq 6000 +platform. +

+ +

Processing and Analysis

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+Reads were mapped to the human genome (hg38) using bwa-meth, deduplicated with Sambamba, +and processed into per-CpG methylation calls. The genome was segmented into 7.1 million +non-overlapping methylation blocks using a multi-channel dynamic programming algorithm +that identifies regions of homogeneous methylation across samples. +

+ +

+Cell-type-specific differentially methylated regions were identified using a one-versus-all +comparison approach. Regions uniquely unmethylated in specific cell types were found to be +enriched for transcriptional enhancers and tissue-specific transcription factor binding motifs. +

+ +

+Data processing was performed using +wgbstools, an open-source +computational suite for DNA methylation sequencing data representation, visualization, +and analysis. +

+ +

Data Access

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+The raw data for these tracks can be explored interactively using the +Table Browser or the +Data Integrator. +For automated analysis, the data may also be queried from our +REST API. +

+ +

+The complete dataset, including all WGBS data files and processed methylation calls, +is available from GEO accession +GSE186458. +

+ +

+For questions regarding the data, please contact +Prof. Tommy Kaplan at the Hebrew +University of Jerusalem. +

+ +

Credits

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+Data generation and analysis were performed at the Hebrew University of Jerusalem by the +Dor, Kaplan, and Glaser laboratories and collaborators. Sample collection involved +collaboration with Hadassah Medical Center, Oregon Health & Science University, +Karolinska Institute, and University of Alberta. +

+ +

References

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+Loyfer N, Magenheim J, Peretz A, Cann G, Bredno J, Klochendler A, Fox-Fisher I, +Shabi-Porat S, Hecht M, Pelet T et al. + +A DNA methylation atlas of normal human cell types. +Nature. 2023 Jan;613(7943):355-364. +PMID: 36599988 +

+ +

+Loyfer N, Rosenski J, Kaplan T. + +wgbstools: a computational suite for DNA methylation sequencing data analysis. +Life Sci Alliance. 2026 Apr;9(4):e202503514. +PMID: 41611450 +

+