4a39786c0e5473f3e987017bcb616b63423a89e2
max
Wed Sep 9 06:16:03 2026 -0700
hg38 Fiber-seq: open the compendium description by naming the collection it belongs to
A subtrack description page should say which collection it is part of and
link back to that page, so a reader who lands on it from a search result
can get to the container. A bare hgTrackUi link rather than one carrying
${hgsid}: native trackDb html is substituted by hgTrackDb when it loads
the table, where there is no cart, so $hgsid resolves to the empty string
and the link would come out as 'hgsid=&g=fiberSeq'. The ${hgsid} form
works on hub pages, which are substituted at render time instead.
refs #36210
diff --git src/hg/makeDb/trackDb/human/hg38/fiberSeqCompendium.html src/hg/makeDb/trackDb/human/hg38/fiberSeqCompendium.html
index f0133557990..b85602a5be4 100644
--- src/hg/makeDb/trackDb/human/hg38/fiberSeqCompendium.html
+++ src/hg/makeDb/trackDb/human/hg38/fiberSeqCompendium.html
@@ -1,233 +1,234 @@
<h2>Description</h2>
<p>
-This track holds the full Fiber-seq data for 41 samples: 14 cell lines and 27 lymphoblastoid
+This track is part of the <a href="hgTrackUi?g=fiberSeq">Fiber-seq</a> 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:
</p>
<ul>
<li>Percent accessible: the fraction of Fiber-seq molecules on which a position was called
accessible, combining both chromosomes.</li>
<li>FIRE peaks: the accessible regulatory elements called from that signal, with a score and
a false discovery rate.</li>
<li>Haplotype accessibility: the percent-accessible signal computed separately for the two
parental chromosomes and drawn as an overlay, which makes elements that are open on one
chromosome but not the other visible directly.</li>
<li>CpG methylation: percent of reads methylated at each CpG, over both chromosomes.</li>
<li>Haplotype CpG: the same measure computed separately for the two parental chromosomes.</li>
<li>CpG haplotype difference: the difference in percent methylation between the two
chromosomes, at four nested significance thresholds.</li>
</ul>
<p>
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.
</p>
<h2>Display Conventions</h2>
<p>
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.
</p>
<p>
In both haplotype overlays:
</p>
<table class="stdTbl">
<tr><th style="background-color:#0072B2;width:2em"> </th><td>Haplotype 1</td></tr>
<tr><th style="background-color:#D55E00;width:2em"> </th><td>Haplotype 2</td></tr>
</table>
<p>
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.
</p>
<p>
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:
</p>
<table class="stdTbl">
<tr><th style="background-color:#898F8F;width:2em"> </th><td>All measured differences, regardless of significance</td></tr>
<tr><th style="background-color:#EBE534;width:2em"> </th><td>p < 0.01</td></tr>
<tr><th style="background-color:#F59416;width:2em"> </th><td>p < 0.001</td></tr>
<tr><th style="background-color:#FF0000;width:2em"> </th><td>p < 0.0001</td></tr>
</table>
<p>
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.
</p>
<p>
The color swatches next to the Sample class filters are:
</p>
<table class="stdTbl">
<tr><th style="background-color:#0072B2;width:2em"> </th>
<td>HPRC, a lymphoblastoid (B-lymphocyte, EBV) line from the Human Pangenome Reference
Consortium</td></tr>
<tr><th style="background-color:#D55E00;width:2em"> </th>
<td>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</td></tr>
</table>
<p>
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.
</p>
<h2>Methods</h2>
<p>
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.
</p>
<p>
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.
</p>
<p>
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.
</p>
<p>
The bigWig and bigBed files were downloaded from
<a href="https://s3.kopah.uw.edu/userprod/web/public/hashed.PacBio-Fiber-seq/" target="_blank">the
Stergachis lab data server</a>, 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
<a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/fiberSeq.txt"
target="_blank">makeDoc</a>, and the scripts that fetch the data and generate the track
configuration are in the
<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/fiberSeq"
target="_blank">kent source tree</a>.
</p>
<p>
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.
</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>fiberSeqCompendium</i>.
</p>
<p>
For automated download and analysis, the data are stored in bigWig and bigBed files that can be
downloaded from <a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/fiberSeq/" target="_blank">our
download server</a>, one directory per sample accession. Each directory holds the peaks as
<tt>fire-peaks.ucsc.bb</tt>, the accessibility signal as <tt>all.percent.accessible.bw</tt> with
<tt>hap1.percent.accessible.bw</tt> and <tt>hap2.percent.accessible.bw</tt>, and the methylation
as <tt>cpg.combined.bw</tt>, <tt>cpg.hap1.bw</tt>, <tt>cpg.hap2.bw</tt> and four
<tt>cpg.diffs_*.bw</tt> files. Individual regions or the whole genome annotation can be obtained
using our tools <tt>bigBedToBed</tt> and <tt>bigWigToBedGraph</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 features within a given range, e.g.
<tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/fiberSeq/PM00001/fire-peaks.ucsc.bb
-chrom=chr21 -start=0 -end=100000000 stdout</tt>
</p>
<p>
The mapping from sample accession to sample name and cell type is in
<a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/fiberSeq/fiberSeqCompendium_metadata.tsv"
target="_blank">fiberSeqCompendium_metadata.tsv</a>. The original data can be downloaded from the
<a href="https://s3.kopah.uw.edu/userprod/web/public/hashed.PacBio-Fiber-seq/" target="_blank">Stergachis
lab data server</a>, and the lab maintains its own track hub and documentation at
<a href="https://fiberseq.github.io/" target="_blank">fiberseq.github.io</a>. The FIRE pipeline
is at <a href="https://github.com/fiberseq/FIRE" target="_blank">github.com/fiberseq/FIRE</a>, the
adenine methylation caller at
<a href="https://github.com/fiberseq/fibertools-rs" target="_blank">github.com/fiberseq/fibertools-rs</a>,
and the CpG pileup tool at
<a href="https://github.com/PacificBiosciences/pb-CpG-tools" target="_blank">github.com/PacificBiosciences/pb-CpG-tools</a>.
</p>
<h2>Credits</h2>
<p>
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.
</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://doi.org/10.1101/2024.06.14.599122" 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>