10f0f6d5160a96867ecf534e1b9d138df7d9b796 lrnassar Mon Sep 28 16:17:46 2026 -0700 Fiber-seq: hide the container by default, and split the five GM lines out into a Rare disease sample class. Max asked for superTrack on rather than on show, since the track covers much the same ground as ENCODE DNase and does not earn a slot in everyone's default hg38 view. The five lymphoblastoid lines GM25455, GM25456, GM27730, GM28570 and GM28572 had been filed as Common Cell Line; Andrew Stergachis says they are rare disease cases consented to broad genomic data sharing and the first of a batch the lab intends to keep adding, so SAMPLE_CLASS_COLORS gains a third entry and the facet now reads 20 HPRC, 16 Common Cell Line, 5 Rare disease sample. refs #36210 diff --git src/hg/makeDb/trackDb/human/hg38/fiberSeq.html src/hg/makeDb/trackDb/human/hg38/fiberSeq.html index be3ff652f1c..b4d63ba5c63 100644 --- src/hg/makeDb/trackDb/human/hg38/fiberSeq.html +++ src/hg/makeDb/trackDb/human/hg38/fiberSeq.html @@ -1,94 +1,94 @@ <h2>Description</h2> <p> Fiber-seq maps which parts of each DNA molecule are bound by proteins and which are open. Permeabilized cells are treated with a non-specific adenine methyltransferase that methylates only exposed adenines, so nucleosomes and other bound proteins leave an unmethylated footprint on each molecule. The DNA is then sequenced on a PacBio instrument, which reads the base sequence, the added adenine methylation and the native CpG methylation from the same molecule. Because the reads are long enough to span heterozygous variants, they can be phased to a parental haplotype, giving a chromatin picture for each allele separately rather than an average of the two. </p> <p> The accessibility this measures is the same property that DNase-seq and ATAC-seq measure, and the tracks here can be read much as those are. The difference is that Fiber-seq reports it one molecule at a time, so a value is a fraction of molecules rather than a pile of cut sites, and it can be split by haplotype. </p> <p> This collection holds the Fiber-seq data from the Stergachis and Vollger labs for 41 samples, -covering common cell lines and 20 lymphoblastoid lines from individuals sequenced by the Human -Pangenome Reference Consortium. It contains two tracks: +covering common cell lines, 20 lymphoblastoid lines from individuals sequenced by the Human +Pangenome Reference Consortium, and five rare disease cases. It contains two tracks: </p> <ul> <li><a href="hgTrackUi?db=hg38&g=fiberSeqAcc">Fiber-seq Acc</a> shows percent-accessible chromatin for seven widely used cell lines as a single overlay, so accessibility can be compared across cell types at a glance.</li> <li><a href="hgTrackUi?db=hg38&g=fiberSeqCompendium">Fiber-seq Compendium</a> holds everything, for every one of the 41 samples: the percent-accessible signal, the called FIRE regulatory element peaks, CpG methylation, and per-haplotype versions of the accessibility and the methylation. Samples are selected from a searchable table.</li> </ul> <p> Related data can be found in the <a href="hgTrackUi?db=hg38&g=dnaMethylation">DNA Methylation</a> collection, which holds methylation measured by other assays. </p> <h2>Display Conventions</h2> <p> Both tracks have their own display conventions and color schemes; see the individual track description pages linked above. The accessibility and methylation signals are scaled 0 to 100 percent throughout, so a bar height means the same thing in every track and every sample. The exception is the CpG haplotype difference, which is a difference between two percentages and so runs from -100 to +100. </p> <h2>Methods</h2> <p> The data were generated and processed by the Stergachis and Vollger labs. Details of the assay, the machine learning model that calls regulatory elements from single molecules, and the methylation calling are given on the individual track description pages. </p> <h2>Data Access</h2> <p> The individual track description pages linked above explain how to download the underlying bigWig and bigBed files and how to query them from scripts. </p> <h2>Credits</h2> <p> Thanks to Mitchell Vollger, Andrew Stergachis and Shane Neph at the University of Washington and the University of Utah for generating this data, for packaging it for the browser and for their help in arranging the tracks. </p> <h2>References</h2> <p> Vollger MR, Swanson EG, Neph SJ, Ranchalis J, Munson KM, Ho CH, Cheng YHH, Sedeño-Cortés AE, Fondrie WE, Bohaczuk SC <em>et al</em>. <a href="https://www.ncbi.nlm.nih.gov/pubmed/40501892" target="_blank"> A haplotype-resolved view of human gene regulation</a>. <em>bioRxiv</em>. 2025 Jun 2;. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/40501892" target="_blank">40501892</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157683/" target="_blank">PMC12157683</a> </p> <p> Stergachis AB, Debo BM, Haugen E, Churchman LS, Stamatoyannopoulos JA. <a href="https://www.ncbi.nlm.nih.gov/pubmed/32587015" target="_blank"> Single-molecule regulatory architectures captured by chromatin fiber sequencing</a>. <em>Science</em>. 2020 Jun 26;368(6498):1449-1454. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/32587015" target="_blank">32587015</a> </p>