cc6ef4c74d072de9c22e8bb88ab0e0983e6f2f47 max Wed Jul 22 18:09:16 2026 -0700 lrSv cardSv: switch CARD count fields from carrier counts to allele counts The NIH CARD provider republished the display bigBed with the count columns changed to diploid allele counts (alleleCount = nabecAlleleCount + hbccAlleleCount). Re-downloaded and rebuilt; renamed the schema fields to AC / nabecAc / hbccAc, updated filter ranges (0:702, 0:410, 0:292) and labels to allele counts, and reworded cardSv.html and the lrSv.html summary. Also noted there are no Alzheimer's cases in these cohorts. Re-ran the merge so lrSvAll carries CARD's allele counts. refs #36258 diff --git src/hg/makeDb/trackDb/human/cardSv.html src/hg/makeDb/trackDb/human/cardSv.html index 233ee84f1b0..f479b84cf8e 100644 --- src/hg/makeDb/trackDb/human/cardSv.html +++ src/hg/makeDb/trackDb/human/cardSv.html @@ -1,139 +1,140 @@ <h2>Description</h2> <p> This track shows structural variants (SVs) identified by Oxford Nanopore long-read sequencing of post-mortem brain tissue (prefrontal cortex) from 351 individuals, generated by the NIH Center for Alzheimer's and Related Dementias (NIH CARD) Long-Read Initiative. Structural variants are genomic rearrangements larger than about 50 bp, such as deletions, insertions, inversions and duplications; because they alter or move large stretches of DNA at once they can have outsized effects on gene dosage, gene regulation and DNA methylation compared with single-nucleotide changes. </p> <p> The cohort combines two studies: 205 samples of European ancestry from the North American Brain Expression Consortium (NABEC, dbGaP phs001300) and 146 samples of African and African-admixed ancestry from the NIMH Human Brain Collection Core (HBCC, dbGaP phs000979). The track contains more than 228,000 SVs called against GRCh38: about 127,000 insertions, 102,000 deletions, 431 inversions -and one tandem duplication. Each record carries the number of carrier samples -overall and split by cohort (NABEC and HBCC), together with the allele frequency reported -by the source project. +and one tandem duplication. Each record carries the alternate allele count +overall and split by cohort (NABEC and HBCC), together with the allele frequency +reported by the source project. None of these samples are Alzheimer's disease +cases; the cohorts are population brain-tissue collections. </p> <h2>Display Conventions and Configuration</h2> <p> Items are colored by SV type, matching the other subtracks of the container: <ul> <li><span style="color: rgb(200,0,0);">Deletions (DEL)</span> - red</li> <li><span style="color: rgb(0,0,200);">Insertions (INS)</span> - blue</li> <li><span style="color: rgb(0,160,0);">Duplications (DUP)</span> - green</li> <li><span style="color: rgb(230,140,0);">Inversions (INV)</span> - orange</li> </ul> </p> <p> Insertions are placed at the insertion site with a width of 1 bp, and the length of the inserted sequence is shown as the insertion length; deletions, inversions and the duplication span the affected reference interval. The mouseover shows the variant name, SV type, reference and insertion lengths, -allele frequency and the number of carrier samples split into the NABEC and +allele frequency and the alternate allele count split into the NABEC and HBCC cohorts. Filters are available for SV type, SV length, insertion length, -carrier count, allele frequency, and the number of carriers in each cohort. +allele count, allele frequency, and the allele count in each cohort. </p> <h2>Methods</h2> <p> NABEC samples were sequenced on Oxford Nanopore R9.4.1 and HBCC samples on R10.4.1 PromethION flow cells, with a median read N50 of 27 kb and about 40x average genome coverage. Structural variants were called both from read alignments (minimap2 alignments processed with Sniffles2 v2.3) and from de novo assemblies (Shasta v0.11.1 assemblies phased with HapDup v0.12 and compared to the reference with Hapdiff). Assembly-based calls were merged across samples with Truvari, read-based calls were merged across samples with Sniffles2, and the read and assembly sets were then merged together and across the two cohorts with Truvari. All processing used the Nanopore Analysis Pipeline (NAPU) workflows on the AnVIL/Terra platform; see Kolmogorov et al. 2023 and Billingsley et al. 2024 for details. </p> <p> The display bigBed <tt>NIH_CARD_longReadSVs.bb</tt> was obtained from the NIH CARD browser-track <a href="https://github.com/meredith705/card_genome_browserTrack" target="_blank">GitHub repository</a>. At UCSC it was converted to the shared long-read SV schema (signed lengths made positive, an explicit insertion-length field added, the single <tt>DUP:TANDEM</tt> call folded to <tt>DUP</tt>, and colors reassigned to the container's shared palette) so it matches the other subtracks. The step-by-step commands are recorded in the UCSC makeDoc for this track container: <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/lrSv.txt" target="_blank"> doc/hg38/lrSv.txt</a>. The conversion script and autoSql schema live in <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/lrSv" target="_blank"> makeDb/scripts/lrSv</a>, and the track configuration is in <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/trackDb/human/lrSv.ra" target="_blank">trackDb/human/lrSv.ra</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>cardSv</i>. </p> <p> The annotation is stored as a bigBed file that can be downloaded from <a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/" target="_blank">our download server</a> as <tt>card.bb</tt>. Individual regions or the whole annotation can be obtained with the <tt>bigBedToBed</tt> utility, available from our <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads">utilities page</a>. Example: <tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/card.bb -chrom=chr21 -start=0 -end=100000000 stdout</tt>. </p> <p> The underlying genotype-level calls in VCF format are available under controlled access through dbGaP (NABEC phs001300, substudy phs003181; HBCC phs000979) and can be requested through the AnVIL Data Explorer for the <a href="https://explore.anvilproject.org/datasets/0b740f36-28d6-4e02-8165-ad0e9674bbf6" target="_blank">NABEC</a> and <a href="https://explore.anvilproject.org/datasets/a4e936d1-d81a-475d-95be-b5cd41de921d" target="_blank">HBCC</a> datasets. </p> <h2>Credits</h2> <p> Thanks to the North American Brain Expression Consortium (NABEC), the NIMH Human Brain Collection Core (HBCC), the Banner Sun Health Research Institute Brain and Body Donation Program, and the NIH CARD Long-Read Initiative for generating and sharing this dataset, and to Melissa Meredith for preparing the browser track. This work was supported by the Intramural Research Programs of the NIA, NINDS, NCI, NHGRI and NIMH, and used the NIH STRIDES Initiative and the NIH HPC Biowulf cluster. </p> <h2>References</h2> <p> Billingsley KJ, Meredith M, Daida K, Jerez PA, Negi S, Malik L, Genner RM, Moller A, Zheng X, Gibson SB <em>et al</em>. <a href="https://doi.org/10.1101/2024.12.16.628723" target="_blank"> Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation</a>. <em>bioRxiv</em>. 2024 Dec 17;. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/39764002" target="_blank">39764002</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11702628/" target="_blank">PMC11702628</a> </p> <p> Kolmogorov M, Billingsley KJ, Mastoras M, Meredith M, Monlong J, Lorig-Roach R, Asri M, Alvarez Jerez P, Malik L, Dewan R <em>et al</em>. <a href="https://doi.org/10.1038/s41592-023-01993-x" target="_blank"> Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation</a>. <em>Nat Methods</em>. 2023 Oct;20(10):1483-1492. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/37710018" target="_blank">37710018</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11222905/" target="_blank">PMC11222905</a> </p>