691a2b8981d6db69e8707ea44041c4661cdac97e max Wed Sep 9 06:38:29 2026 -0700 Imprinting: add the ASM Atlas tracks, and tidy the collection's labels Adds a composite built from Rosenski et al. 2025, "Atlas of imprinted and allele-specific DNA methylation in the human body". Three subtracks: the 458 regions whose methylation follows the parent of origin, the 72 known control regions with the boundaries the paper redrew, and the pool of 385,235 regions carrying two methylation states that those came out of. A fourth set, the regions whose methylation follows a nearby SNP, is built by the scripts but its stanza is commented out, since sequence driven methylation is not imprinting. The authors released hg19 only, so all three are lifted. Their published files are close to bare BED, so the SNPs, cell types, p-values, gene links and gamete methylation on the details pages are read out of the paper's supplementary tables and joined on by position. Regions that lift but change length by more than 10%, because hg38 added sequence inside them, are kept with a note rather than dropped: one of them is TCEB3C, the only control region on chr18. Also across the collection: - long labels name their source right after "Imprinting", so that a label read on its own says where the data came from - the two gene catalogs are worded alike, and ordered OMIM, Geneimprint, MethBase2, Akbari, ASM Atlas - the OMIM curators confirmed that their (I) marker covers established and candidate imprinted genes alike, with nothing in the export to tell them apart. Labels, description page and makeDoc now say so, and the claim that the set is "more conservative" than the computational tracks is gone. The bigBed was rebuilt for the autoSql line, same 459 features. - every subtrack page opens by naming the collection, linked back to its hgTrackUi page, and no longer repeats the collection page's introduction to imprinting refs #37599 diff --git src/hg/makeDb/trackDb/human/hg38/methBaseAsm.html src/hg/makeDb/trackDb/human/hg38/methBaseAsm.html index 7c86132d86f..880c29b42ef 100644 --- src/hg/makeDb/trackDb/human/hg38/methBaseAsm.html +++ src/hg/makeDb/trackDb/human/hg38/methBaseAsm.html @@ -1,146 +1,144 @@ <h2>Description</h2> <p> -This track summarizes allele-specific methylation (ASM) across thousands of human methylomes. At an imprinted locus one allele carries methylated CpG sites and the other does not, so a region that shows this pattern in many independent samples is a candidate imprinting control region. +Part of the <a href="hgTrackUi?db=$db&hgsid=${hgsid}&g=$parentTrack">Imprinting</a> track collection, this track summarizes allele-specific methylation (ASM) across +thousands of human methylomes. A region where one allele is methylated and the other is not in many +independent samples is a candidate imprinting control region. </p> <p> Whole-genome bisulfite sequencing reads are long enough to cover several CpG sites at once, so the methylation states of neighboring sites on the same DNA molecule can be read together. When the reads at a locus fall into two clearly separated groups, one mostly methylated and one mostly unmethylated, that locus shows allele-specific methylation. This track shows, for every CpG site in the genome, the fraction of MethBase2 human methylomes in which the site was called part of an allelically methylated region (AMR). Values range from 0 to 1. High values mean that many independent samples show two differently methylated alleles at that site, which is the expected signature of a constitutive imprinted region. Lower but non-zero values point at loci where allele-specific methylation is restricted to certain tissues or is driven by sequence variation rather than by imprinting. </p> <p> The score is a summary across samples, not a measurement in any single sample. A value of 0.5 does not mean that half of the DNA molecules are methylated; it means that about half of the surveyed methylomes had an AMR call covering that CpG. </p> -<p> -See the <a href="hgTrackUi?g=imprinting">Imprinting</a> track collection page for background on genomic imprinting and for the color scheme shared by all of its subtracks. -</p> - <h2>Display Conventions and Configuration</h2> <p> The track is a signal graph with one value per CpG site. The default vertical range is 0 to 0.5, since even the strongest known imprinted regions rarely exceed that fraction across a set of samples this diverse. The range, the graph height, the smoothing window and the aggregation function can all be changed on the track configuration page. Positions between CpG sites carry no data and are drawn as gaps. </p> <p> The per-sample methylation levels, hypomethylated regions and read coverage that this summary is derived from are published by the Smith lab as a track hub, which can be loaded from <a href="http://smithlab.usc.edu/trackdata/methylation/hub.txt" target="_blank">http://smithlab.usc.edu/trackdata/methylation/hub.txt</a> or found in the list of public hubs. </p> <h2>Methods</h2> <p> MethBase2 collects publicly available whole-genome bisulfite sequencing runs from the <a href="https://www.ncbi.nlm.nih.gov/sra" target="_blank">NCBI Sequence Read Archive</a> and reprocesses all of them through one uniform pipeline built on <a href="https://dnmtools.readthedocs.io/" target="_blank">DNMTools</a>. Reads are mapped with abismal, duplicates are removed, the bisulfite conversion rate is estimated, and per-site methylation levels are computed and symmetrized across the two strands of each CpG. A methylome enters the browsable collection only if its bisulfite conversion rate is at least 0.9 and at least 70% of CpG sites are covered, which in practice implies well above 1x average coverage. Allelically methylated regions are then called per methylome with the <a href="https://dnmtools.readthedocs.io/en/latest/amrfinder/" target="_blank">amrfinder</a> command. It slides a window of 10 CpG sites along the genome and fits two models to the reads in each window, one in which both alleles share a single methylation state and one in which the two alleles have separate states. The window is called an AMR when the two-allele model fits significantly better, using a likelihood ratio test with a false discovery rate correction; windows need at least 4x mean coverage per CpG to be tested, and neighboring AMRs closer than 1 kb are merged. The statistical model is described in Fang et al. (2012). The score shown here is the number of methylomes whose AMR set covers a given CpG, divided by the number of methylomes tested. </p> <p> The data file was downloaded from <a href="http://smithlab.usc.edu/methbase/data/common/hg38/hg38.asm.bw" target="_blank">http://smithlab.usc.edu/methbase/data/common/hg38/hg38.asm.bw</a>, the "common" section of the MethBase2 track hub at <a href="http://smithlab.usc.edu/trackdata/methylation/hub.txt" target="_blank">http://smithlab.usc.edu/trackdata/methylation/hub.txt</a>. The file is a bigWig and was used as it is, without reformatting or recalculation. It carries a value at each of 29,401,795 CpG positions, on chr1 to chr22, chrX, chrY and chrM. The steps we ran are documented in the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/imprinting.txt" target="_blank">imprinting makeDoc</a>. </p> <h2>Data Access</h2> <p> The data can be explored interactively in table format with the <a href="../cgi-bin/hgTables">Table Browser</a> or the <a href="../cgi-bin/hgIntegrator">Data Integrator</a> and exported from there to spreadsheet or tab-sep tables. From scripts, the data can be accessed through our <a href="https://api.genome.ucsc.edu">API</a>, track=<i>methBaseAsm</i>. </p> <p> For automated download and analysis, the annotation is stored in a bigWig file that can be downloaded from <a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/imprinting/methBaseAsm/" target="_blank">our download server</a>. The file for this track is called <tt>hg38.asm.bw</tt>. Individual regions or the whole annotation can be obtained using our tools <tt>bigWigToBedGraph</tt> or <tt>bigWigSummary</tt>, which can be compiled from the source code or downloaded as precompiled binaries for your system. Instructions for downloading source code and binaries can be found <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads">here</a>. The tools can also be used to obtain values within a given range, e.g. <tt>bigWigToBedGraph http://hgdownload.soe.ucsc.edu/gbdb/hg38/imprinting/methBaseAsm/hg38.asm.bw -chrom=chr15 -start=24950000 -end=25200000 stdout</tt> </p> <p> The original data can be downloaded from the MethBase2 track hub at <a href="http://smithlab.usc.edu/trackdata/methylation/hub.txt" target="_blank">http://smithlab.usc.edu/trackdata/methylation/hub.txt</a>. </p> <h2>Credits</h2> <p> Thanks to Andrew D. Smith and the Smith lab at the University of Southern California for building MethBase2 and for making the allele-specific methylation summary available. The collection is maintained at <a href="https://github.com/smithlabcode/methbase" target="_blank">https://github.com/smithlabcode/methbase</a>. </p> <h2>References</h2> <p> Fang F, Hodges E, Molaro A, Dean M, Hannon GJ, Smith AD. <a href="https://www.ncbi.nlm.nih.gov/pubmed/22523239" target="_blank"> Genomic landscape of human allele-specific DNA methylation</a>. <em>Proc Natl Acad Sci U S A</em>. 2012 May 8;109(19):7332-7. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/22523239" target="_blank">22523239</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3358917/" target="_blank">PMC3358917</a> </p> <p> Song Q, Decato B, Hong EE, Zhou M, Fang F, Qu J, Garvin T, Kessler M, Zhou J, Smith AD. <a href="https://www.ncbi.nlm.nih.gov/pubmed/24324667" target="_blank"> A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics</a>. <em>PLoS One</em>. 2013;8(12):e81148. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/24324667" target="_blank">24324667</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3855694/" target="_blank">PMC3855694</a> </p>