-This track holds the full Fiber-seq data for 41 samples: 14 cell lines and 27 lymphoblastoid +This track is part of the Fiber-seq collection. It holds the +full Fiber-seq data for 41 samples: 14 cell lines and 27 lymphoblastoid lines derived from individuals sequenced by the Human Pangenome Reference Consortium and the Genome in a Bottle project. Chromatin accessibility and CpG methylation are read from the same molecules in the same experiment, so both are kept in one table here and can be compared without worrying about differences in cell preparation or sequencing depth. Six kinds of data are available for each sample:
Because 41 samples times six kinds of data is far too many tracks for a checkbox list, samples are chosen from a searchable table on this page. Pick the kinds of data you want along the top, then select samples in the table; the browser turns on that combination for every sample you picked, and keeps each sample's tracks together in the display. Sample class has filter checkboxes in the panel to the left, and every column can be searched with the box under its heading and clicked to sort, so a sample can be found by name, cell type, sample class or accession.
Accessibility, methylation and the haplotype overlays are all drawn 0 to 100 percent on a fixed scale, so heights are comparable between samples and between the two assays. The accessibility tracks use maximum as the windowing function, so a narrow element survives zooming out, while the methylation tracks use mean, since an average is the meaningful summary for a methylation level. FIRE peaks are shown in dense mode by default, one row per sample.
In both haplotype overlays:
| Haplotype 1 | |
| Haplotype 2 |
The two are overlaid transparently, so a position with equal signal on both chromosomes appears as the two colors superimposed, and a haplotype-selective element appears as one color standing alone. Which parental chromosome is haplotype 1 is arbitrary and is not consistent between samples.
The CpG haplotype difference track runs from -100 to +100 percent, so a bar above the midline means haplotype 1 is more methylated and a bar below it means haplotype 2 is. It is a stack of four overlaid signals, one per significance threshold, drawn least significant first so that the more significant levels are painted on top:
| All measured differences, regardless of significance | |
| p < 0.01 | |
| p < 0.001 | |
| p < 0.0001 |
The thresholds are nested, so a position drawn red also belongs to all three looser sets. Reading the track amounts to reading the color: grey is noise, red is a strong difference between the two chromosomes at that CpG.
The color swatches next to the Sample class filters are:
| HPRC, a lymphoblastoid (B-lymphocyte, EBV) line from the Human Pangenome Reference Consortium | |
| Common cell line, which here also covers GM12878 and HG002: both are lymphoblastoid, but they come from ENCODE and from Genome in a Bottle rather than from the consortium |
Peaks carry two filterable values, the FIRE score in the signalValue field and the false discovery rate as a -log10 value in the qValue field, and both can be filtered from a peak track's own configuration page, along with the score. No filter is applied by default. A short tick inside each peak marks the point source, the single base the pipeline picked as the summit. Switching a peak track to pack or full also gives each peak a mouseover with its FIRE score and FDR; dense mode has no per-peak hover, which is a property of dense display rather than of this track. The pValue field of the source files is set to -1 throughout and carries no information.
Permeabilized cells were treated with the Hia5 N6-adenine methyltransferase, which methylates adenines in DNA not protected by a bound protein, and high molecular weight DNA was prepared into PacBio SMRTbell libraries and sequenced. The adenine methylation added by the enzyme is chemically distinct from native CpG methylation, so both are read from the same molecule. Adenine methylation was called with fibertools-rs v0.4, and reads were aligned and haplotype-phased; for GM12878 an average 20 kb read spans at least one heterozygous variant and 87.9 percent of reads could be phased against GRCh38.
The FIRE pipeline v0.0.4, a Snakemake workflow, applied a semi-supervised XGBoost classifier to label methyltransferase-sensitive patches on each read as FIRE elements. The classifier was trained with Mokapot over 15 iterations on 21 GM12878 experiments spanning 5.8 to 13.3 percent adenine methylation, with DNase I and CTCF ChIP-seq peaks as mixed-positive labels. The aggregate FIRE score at a position is -50/R times the sum over covering elements of log10(1 - min(EP, 0.99)), where R is the read depth and EP the estimated precision of each element, which puts the score between 0 and 100; positions covered by fewer than four FIRE elements are not scored. The false discovery rate was estimated by shuffling whole reads within a chromosome and comparing the resulting score distribution to the observed one. Peaks are FIRE score local maxima below a 5 percent FDR with at least 10 percent of covering reads actuated; adjacent maxima sharing half their elements or overlapping reciprocally by 90 percent were merged, and peak boundaries were set to the median start and end of the underlying elements.
Base-level CpG methylation was called with jasmine, and the percent methylation at each genomic position was computed from a pileup of reads using pb-CpG-tools. Reads were haplotype-phased before the pileup, which gives the per-haplotype values, and the difference track is the subtraction of one haplotype from the other with the per-position significance thresholds applied. See Vollger et al. for the full description of all of the above.
The bigWig and bigBed files were downloaded from the Stergachis lab data server, eleven files per sample. The signal files were copied without modification. The peak files were rebuilt, because their bigBed header recorded three data columns while the data has the full ten of a narrowPeak file, which left the browser unable to see the signalValue and qValue columns for filtering or display. The rebuild corrects the header and rounds the FIRE score and the FDR to three decimals, which is well beyond the precision either measure carries. It does drop peaks called on chrEBV, the Epstein-Barr virus decoy of the GRCh38 analysis set, since that sequence is not part of hg38: 421 of 9,487,043 peaks, in 20 of the 41 samples, between 2 and 166 peaks each, leaving 9,486,622 in the track. The download, rebuild and integrity steps are documented in the makeDoc, and the scripts that fetch the data and generate the track configuration are in the kent source tree.
Two files are empty at the source: the haplotype accessibility bigWigs for GM12878 (accession PM00001), so its haplotype accessibility overlay shows no data. Its combined signal, its peaks and all of its CpG methylation are present, and every other sample has real haplotype files.
The data can be explored interactively in table format with the Table Browser or the Data Integrator and exported from there to spreadsheet or tab-sep tables. From scripts, the data can be accessed through our API, track=fiberSeqCompendium.
For automated download and analysis, the data are stored in bigWig and bigBed files that can be downloaded from our download server, one directory per sample accession. Each directory holds the peaks as fire-peaks.ucsc.bb, the accessibility signal as all.percent.accessible.bw with hap1.percent.accessible.bw and hap2.percent.accessible.bw, and the methylation as cpg.combined.bw, cpg.hap1.bw, cpg.hap2.bw and four cpg.diffs_*.bw files. Individual regions or the whole genome annotation can be obtained using our tools bigBedToBed and bigWigToBedGraph, 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 here. The tools can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/fiberSeq/PM00001/fire-peaks.ucsc.bb -chrom=chr21 -start=0 -end=100000000 stdout
The mapping from sample accession to sample name and cell type is in fiberSeqCompendium_metadata.tsv. The original data can be downloaded from the Stergachis lab data server, and the lab maintains its own track hub and documentation at fiberseq.github.io. The FIRE pipeline is at github.com/fiberseq/FIRE, the adenine methylation caller at github.com/fiberseq/fibertools-rs, and the CpG pileup tool at github.com/PacificBiosciences/pb-CpG-tools.
Thanks to Mitchell Vollger, Andrew Stergachis and Shane Neph for generating this data, for assembling it into track hubs and for their help in arranging these tracks for the browser.
Vollger MR, Swanson EG, Neph SJ, Ranchalis J, Munson KM, Ho CH, Cheng YHH, Sedeño-Cortés AE, Fondrie WE, Bohaczuk SC et al. A haplotype-resolved view of human gene regulation. bioRxiv. 2025 Jun 2;. PMID: 40501892; PMC: PMC12157683
Stergachis AB, Debo BM, Haugen E, Churchman LS, Stamatoyannopoulos JA. Single-molecule regulatory architectures captured by chromatin fiber sequencing. Science. 2020 Jun 26;368(6498):1449-1454. PMID: 32587015