60410f74b7cbc4fbad91bcdbe4e0276e44d936ee lrnassar Fri Feb 6 09:51:04 2026 -0800 Tweaks to the methylation atlas track based on author feedback, refs #36826 diff --git src/hg/makeDb/trackDb/human/humanMethylationAtlas.html src/hg/makeDb/trackDb/human/humanMethylationAtlas.html index 418eede8bae..4a6449076ad 100755 --- src/hg/makeDb/trackDb/human/humanMethylationAtlas.html +++ src/hg/makeDb/trackDb/human/humanMethylationAtlas.html @@ -1,185 +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. +Loyfer N, Rosenski J, Kaplan T. +<a href="https://doi.org/10.26508/lsa.202503514" target="_blank"> +wgbstools: a computational suite for DNA methylation sequencing data analysis</a>. +<em>Life Sci Alliance</em>. 2026 Apr;9(4):e202503514. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/41611450" target="_blank">41611450</a> </p>