a328f4122a06272bb3d76a1f8297e8f5f264e70e lrnassar Thu Feb 5 16:51:06 2026 -0800 First draft of the human methylation atlas track, refs #36826 diff --git src/hg/makeDb/trackDb/human/humanMethylationAtlas.html src/hg/makeDb/trackDb/human/humanMethylationAtlas.html new file mode 100755 index 00000000000..418eede8bae --- /dev/null +++ src/hg/makeDb/trackDb/human/humanMethylationAtlas.html @@ -0,0 +1,185 @@ +<h2>Description</h2> +<p> +The <b>Human Methylation Atlas</b> tracks display genome-wide DNA methylation profiles from +deep whole-genome bisulfite sequencing (WGBS) of <b>39 primary human cell types</b> +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. +</p> + +<p> +The atlas comprises two track sets: +</p> + +<ul> + <li><b>Human Methylation Atlas Summary</b> - Contains cell-type-specific marker regions + identified from the atlas, including uniquely unmethylated loci and putative enhancer + regions annotated with regulatory features.</li> + <li><b>Human Methylation Atlas Signals</b> - Contains per-cell-type methylation signal + tracks (bigWig format) showing methylation beta values (0-1 scale) across the genome, + with merged tracks for each cell type and individual sample replicates.</li> +</ul> + +<p> +DNA methylation patterns are highly reproducible across individuals of the same cell type +(>99.5% identical), reflecting the robustness of cell identity programs. Unsupervised +clustering of these methylomes recapitulates key elements of tissue ontogeny and +developmental lineage relationships. +</p> + +<h2>Display Conventions and Configuration</h2> + +<h3>Signal Tracks</h3> +<p> +Signal tracks display methylation beta values on a 0-1 scale, where 0 indicates fully +unmethylated CpGs and 1 indicates fully methylated CpGs. A value of -1 indicates +missing data. For optimal comparison across cell types, set the vertical viewing range +to 0-1 with auto-scale off. +</p> + +<p> +Merged signal tracks aggregate data across biological replicates for each cell type. +Individual replicate tracks are available for detailed analysis. +</p> + +<h3>Track Colors</h3> +<p> +Tracks are colored by tissue/cell type category as follows: +</p> + +<table border="1" cellpadding="4" cellspacing="0"> +<tr><th>Color</th><th>Cell Type(s)</th></tr> +<tr><td style="background-color:rgb(138,43,226);width:30px;"> </td><td>Neurons</td></tr> +<tr><td style="background-color:rgb(148,103,189);width:30px;"> </td><td>Oligodendrocytes</td></tr> +<tr><td style="background-color:rgb(72,61,139);width:30px;"> </td><td>Thyroid Epithelium</td></tr> +<tr><td style="background-color:rgb(153,50,204);width:30px;"> </td><td>Prostate Epithelium</td></tr> +<tr><td style="background-color:rgb(186,85,211);width:30px;"> </td><td>Bladder Epithelium</td></tr> +<tr><td style="background-color:rgb(220,20,60);width:30px;"> </td><td>Heart Cardiomyocytes</td></tr> +<tr><td style="background-color:rgb(205,92,92);width:30px;"> </td><td>Smooth Muscle</td></tr> +<tr><td style="background-color:rgb(178,34,34);width:30px;"> </td><td>Heart Fibroblasts</td></tr> +<tr><td style="background-color:rgb(139,0,0);width:30px;"> </td><td>Skeletal Muscle</td></tr> +<tr><td style="background-color:rgb(205,51,51);width:30px;"> </td><td>Erythrocyte Progenitors</td></tr> +<tr><td style="background-color:rgb(255,99,71);width:30px;"> </td><td>Blood Granulocytes</td></tr> +<tr><td style="background-color:rgb(244,164,96);width:30px;"> </td><td>Blood Monocytes/Macrophages</td></tr> +<tr><td style="background-color:rgb(255,140,0);width:30px;"> </td><td>Blood T Cells</td></tr> +<tr><td style="background-color:rgb(255,165,0);width:30px;"> </td><td>Blood B Cells</td></tr> +<tr><td style="background-color:rgb(255,127,80);width:30px;"> </td><td>Blood NK Cells</td></tr> +<tr><td style="background-color:rgb(255,215,0);width:30px;"> </td><td>Pancreas Beta Cells</td></tr> +<tr><td style="background-color:rgb(218,165,32);width:30px;"> </td><td>Pancreas Alpha Cells</td></tr> +<tr><td style="background-color:rgb(240,230,140);width:30px;"> </td><td>Pancreas Delta Cells</td></tr> +<tr><td style="background-color:rgb(238,232,170);width:30px;"> </td><td>Pancreas Duct Cells</td></tr> +<tr><td style="background-color:rgb(189,183,107);width:30px;"> </td><td>Pancreas Acinar Cells</td></tr> +<tr><td style="background-color:rgb(34,139,34);width:30px;"> </td><td>Colon Epithelium</td></tr> +<tr><td style="background-color:rgb(85,107,47);width:30px;"> </td><td>Colon Fibroblasts</td></tr> +<tr><td style="background-color:rgb(46,139,87);width:30px;"> </td><td>Small Intestine Epithelium</td></tr> +<tr><td style="background-color:rgb(60,179,113);width:30px;"> </td><td>Gastric Epithelium</td></tr> +<tr><td style="background-color:rgb(107,142,35);width:30px;"> </td><td>Gallbladder</td></tr> +<tr><td style="background-color:rgb(139,69,19);width:30px;"> </td><td>Liver Hepatocytes</td></tr> +<tr><td style="background-color:rgb(100,149,237);width:30px;"> </td><td>Lung Bronchus Epithelium</td></tr> +<tr><td style="background-color:rgb(135,206,250);width:30px;"> </td><td>Lung Alveolar Epithelium</td></tr> +<tr><td style="background-color:rgb(255,140,105);width:30px;"> </td><td>Kidney Epithelium</td></tr> +<tr><td style="background-color:rgb(255,105,180);width:30px;"> </td><td>Endothelial</td></tr> +<tr><td style="background-color:rgb(219,112,147);width:30px;"> </td><td>Breast Basal Epithelium</td></tr> +<tr><td style="background-color:rgb(255,182,193);width:30px;"> </td><td>Breast Luminal Epithelium</td></tr> +<tr><td style="background-color:rgb(218,112,214);width:30px;"> </td><td>Fallopian Epithelium</td></tr> +<tr><td style="background-color:rgb(221,160,221);width:30px;"> </td><td>Ovary Epithelium</td></tr> +<tr><td style="background-color:rgb(210,180,140);width:30px;"> </td><td>Adipocytes</td></tr> +<tr><td style="background-color:rgb(222,184,135);width:30px;"> </td><td>Epidermal Keratinocytes</td></tr> +<tr><td style="background-color:rgb(245,222,179);width:30px;"> </td><td>Dermal Fibroblasts</td></tr> +<tr><td style="background-color:rgb(188,143,143);width:30px;"> </td><td>Bone Osteoblasts</td></tr> +<tr><td style="background-color:rgb(0,206,209);width:30px;"> </td><td>Head Neck Epithelium</td></tr> +</table> + +<h2>Methods</h2> + +<h3>Sample Collection and Sequencing</h3> +<p> +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. +</p> + +<p> +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. +</p> + +<h3>Processing and Analysis</h3> +<p> +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. +</p> + +<p> +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. +</p> + +<p> +Data processing was performed using +<a href="https://github.com/nloyfer/wgbs_tools" target="_blank">wgbstools</a>, an open-source +computational suite for DNA methylation sequencing data representation, visualization, +and analysis. +</p> + +<h2>Data Access</h2> +<p> +The raw data for these tracks can be explored interactively using the +<a href="../cgi-bin/hgTables">Table Browser</a> or the +<a href="../cgi-bin/hgIntegrator">Data Integrator</a>. +For automated analysis, the data may also be queried from our +<a href="../goldenPath/help/api.html">REST API</a>. +</p> + +<p> +The complete dataset, including all WGBS data files and processed methylation calls, +is available from GEO accession +<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186458" +target="_blank">GSE186458</a>. +</p> + +<p> +For questions regarding the data, please contact +<a href="mailto:tommy.kaplan@mail.huji.ac.il">Prof. Tommy Kaplan</a> at the Hebrew +University of Jerusalem. +</p> + +<h2>Credits</h2> +<p> +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. +</p> + +<p> +Track development and UCSC Genome Browser integration by the +<a href="../contacts.html">UCSC Genome Browser Group</a>. +</p> + +<h2>References</h2> +<p> +Loyfer N, Magenheim J, Peretz A, Cann G, Bredno J, Klochendler A, Fox-Fisher I, +Shabi-Porat S, Hecht M, Pelet T <em>et al</em>. +<a href="https://www.nature.com/articles/s41586-022-05580-6" target="_blank"> +A DNA methylation atlas of normal human cell types</a>. +<em>Nature</em>. 2023 Jan;613(7943):355-364. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36599988" target="_blank">36599988</a> +</p> + +<p> +Loyfer N, Magenheim J, Dor Y, Kaplan T. +<a href="https://www.biorxiv.org/content/10.1101/2024.05.08.593132" target="_blank"> +wgbstools: a computational suite for DNA methylation sequencing data representation, +visualization, and analysis</a>. +<em>bioRxiv</em>. 2024. +</p> +