710dd5a15d434aa67588901bd85a5ec1dfb26357 gperez2 Mon Mar 9 18:11:59 2026 -0700 Forgot to commit the Human Methylation Atlas Signals track description page, and remove the humanMethylationAtlas.ra/humanMethylationAtlas.html files in my last commit for the DNA Methylation track, refs #36826 diff --git src/hg/makeDb/trackDb/human/humanMethylationAtlas.html src/hg/makeDb/trackDb/human/methylationAtlasSignals.html similarity index 89% rename from src/hg/makeDb/trackDb/human/humanMethylationAtlas.html rename to src/hg/makeDb/trackDb/human/methylationAtlasSignals.html index 4a6449076ad..c7c010dcfa0 100755 --- src/hg/makeDb/trackDb/human/humanMethylationAtlas.html +++ src/hg/makeDb/trackDb/human/methylationAtlasSignals.html @@ -1,185 +1,176 @@ <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: +The 205 samples from 39 cell type groups are organized into two data types: </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> + <li><b>Merged</b> tracks display the combined methylation signal across all biological replicates + for a given cell type, providing one representative track per cell type.</li><br> + <li><b>Replicate</b> tracks display the methylation signal for each individual sample and are + available for detailed analysis.</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. +(>99.5% identical), reflecting the robustness of cell identity programs. </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. +DNA methylation analysis. Some cell types showed lower purity, including colon fibroblasts (78%), +smooth muscle cells (82%), endothelial cells (86%), and adipocytes (87%). +</p> + +<p> +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. </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, 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>