Structural variants (Cancer GIAB)

JBrowse v5 beta

The tutorials target the JBrowse v5 beta, and the v4.3.0 release on the download page lacks some of what they show. To install the beta of JBrowse Web, run npm install -g @jbrowse/cli@next, then jbrowse create jbrowse2 --branch v5.0.0-beta.10. Desktop beta builds are coming soon.

The Cancer Genome in a Bottle project publishes HG008, a matched tumor/normal PDAC cell line, as PacBio HiFi reads, a draft SV and CNV benchmark, and a telomere-to-telomere assembly of the tumor. We load them as JBrowse tracks and read each benchmark call against the alignments and the copy number under it. We:

load the benchmark SV and CNV calls beside four further published callsets
read one chr3-chr13 translocation three ways: the caller's breakend, the reads that span it, and the tumor assembly that resolves it onto one contig
check copy number at four driver genes, CDKN2A, TP53, SMAD4 and KRAS, against depth and B-allele frequency
align the tumor assembly to GRCh38 and view the same rearrangement as synteny and a dotplot

Prerequisites

The walkthroughs at the end run on the hosted C-GIAB demo with none of this. Building your own instance needs:

A machine with HTTP access, either a public URL or http://localhost
~1 TB of free disk for the tracks, or ~1.5 TB for the full pipeline
At least 32 GB of RAM for the minimap2 step; only data preparation needs it.
The command-line tools below, with the versions tested in parentheses:
- JBrowse CLI (@jbrowse/cli v3.6.5 or later)
- Node.js (v18 minimum, v24.1.0 used for this tutorial)
- tabix (v1.21 or later)
- samtools (v1.21 or later)
- minimap2
- megadepth (v1.2.0 or later), for the coverage tracks
- HiFiCNV (v1.0 or later), for the binned depth track

Where the data comes from

HG008, the C-GIAB matched tumor/normal pair, under NCBI BioProject PRJNA200694 on the C-GIAB FTP. The assemblies are on NIST's S3 bucket, and the per-clone CNV calls are rehosted here.

The reference and the reads:

the C-GIAB reference build (GRCh38 with decoys and masked regions): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/references/GRCh38/GRCh38_GIABv3_no_alt_analysis_set_maskedGRC_decoys_MAP2K3_KMT2C_KCNJ18.fasta.gz
the tumor/normal PacBio HiFi reads (Revio run, 116x tumor, 35x normal): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/PacBio_Revio_20240125/

The somatic call sets, one per group that published one:

the V0.5 draft benchmark SV and CNV calls: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/
Severus somatic SVs (HiFi): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi_Severus-SV_20240308/somatic_SVs/severus_somatic.vcf.gz
the minda ensemble SVs (HiFi, ONT and Illumina callers): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH-NCI_minda-ensemble_20240710/HG008_minda_ensemble.vcf
DRAGEN's somatic SV and CNV calls (Illumina): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/
NYGC's somatic SVs, annotated CNV segments and BIC-seq2 log2 ratio (Illumina): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/
Wakhan's haplotype-specific copy number and LOH segments (HiFi phased with Hi-C): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi-HiC_Wakhan-CNA_20240424/bed_output/
the earlier Wakhan run's Clair3 tumor small-variant calls: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi_Wakhan-CNA_20240308/vcf_inputs/merge_output_tumor.vcf.gz
the normal's germline calls: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/PacBio_Revio_20240125/pacbio-wgs-wdl_germline_20240206/HG008-N-P.GRCh38.deepvariant.phased.vcf.gz

The assemblies:

the T2T tumor assembly, v3.2: https://nist-giab.s3.us-east-1.amazonaws.com/giab_tumor-normal/analysis/HG008/NIST_asm_dev/HG008T_v3.2/HG008T_v3.2.fasta.gz
the matched normal assembly, v6.3: https://nist-giab.s3.us-east-1.amazonaws.com/giab_tumor-normal/analysis/HG008/NIST_asm_dev/HG008N_v6.3/HG008N_v6.3.fasta.gz

The single-cell-derived clone panel:

short-read WGS, one run per clone: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/NIST/HG008-T_clones/
the per-clone CNVkit calls, rehosted merged into one multi-row BED: https://jbrowse.org/demos/cgiab/HG008T-clones.cnv.multirow.bed.gz

The C-GIAB dataset

C-GIAB publishes HG008 from one pancreatic ductal adenocarcinoma (PDAC) donor, as the tumor (HG008-T) and normal pancreatic tissue (HG008-N-P). HG008-T is hypodiploid, with 35 tumor chromosomes down from 46, widespread arm-level loss, and truncal interchromosomal rearrangements (Wagner et al. 2026).

Setting up

The instance itself is the web quickstart unchanged. Two of the prerequisites install from release binaries:

wget https://github.com/ChristopherWilks/megadepth/releases/download/1.2.0/megadepth
chmod +x megadepth && sudo mv megadepth /usr/local/bin/
# --strip-components drops the release's top-level folder and --wildcards
# extracts only the binary, straight into the install directory
curl -L https://github.com/PacificBiosciences/HiFiCNV/releases/download/v1.0.1/hificnv-v1.0.1-x86_64-unknown-linux-gnu.tar.gz \
  | tar xz --strip-components=1 -C /usr/local/bin --wildcards '*/hificnv'

Reproduce it end to end builds this instance in one script; the sections below give the track config for each file.

The benchmark SV and CNV calls

The V0.5 HG008-T draft benchmark is two files, the SV calls as an indexed VCF and the CNV calls as a BED, both loaded straight from their FTP URL.

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_benchmark_sv",
  "name": "HG008-T V0.5 draft benchmark somatic SVs",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-stvar_PASS.draftbenchmark.vcf.gz"
  }
}

CLI (add-track)

jbrowse add-track https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-stvar_PASS.draftbenchmark.vcf.gz \
  --trackId hg008t_benchmark_sv \
  --name "HG008-T V0.5 draft benchmark somatic SVs" \
  --assemblyNames GRCh38_GIABv3

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_benchmark_sv",
  "name": "HG008-T V0.5 draft benchmark somatic SVs",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-stvar_PASS.draftbenchmark.vcf.gz"
  }
}

The CNV BED ships without a header; name its columns with columnNames. A threshold colour over total_copy_number paints each call by its copy number with a key, the same palette as the subclonal lane further down, and a labels.name expression prints each call's copy number with its haplotype split:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "hg008t_somatic_cnv",
  "name": "HG008-T somatic CNV",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-CNV_PASS.draftbenchmark.calls.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "total_copy_number",
      "hap1_copy_number",
      "hap2_copy_number",
      "name"
    ]
  },
  "displayDefaults": {
    "labels": {
      "name": "jexl:'CN '+get(feature,'total_copy_number')+' ('+get(feature,'hap1_copy_number')+'|'+get(feature,'hap2_copy_number')+')'"
    },
    "color": {
      "field": "total_copy_number",
      "scale": "threshold",
      "domain": ["1", "2", "3", "4"],
      "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
      "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
      "title": "Copy number"
    }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "hg008t_somatic_cnv",
  "name": "HG008-T somatic CNV",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-CNV_PASS.draftbenchmark.calls.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "total_copy_number",
      "hap1_copy_number",
      "hap2_copy_number",
      "name"
    ]
  },
  "displayDefaults": {
    "labels": {
      "name": "jexl:'\''CN '\''+get(feature,'\''total_copy_number'\'')+'\'' ('\''+get(feature,'\''hap1_copy_number'\'')+'\''|'\''+get(feature,'\''hap2_copy_number'\'')+'\'')'\''"
    },
    "color": {
      "field": "total_copy_number",
      "scale": "threshold",
      "domain": ["1", "2", "3", "4"],
      "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
      "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
      "title": "Copy number"
    }
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "hg008t_somatic_cnv",
  "name": "HG008-T somatic CNV",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIST_HG008-T_somatic-stvar-CNV_DraftBenchmark_V0.5-20260318/GRCh38_HG008-T-V0.5_somatic-CNV_PASS.draftbenchmark.calls.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "total_copy_number",
      "hap1_copy_number",
      "hap2_copy_number",
      "name"
    ]
  },
  "displayDefaults": {
    "labels": {
      "name": "jexl:'CN '+get(feature,'total_copy_number')+' ('+get(feature,'hap1_copy_number')+'|'+get(feature,'hap2_copy_number')+')'"
    },
    "color": {
      "field": "total_copy_number",
      "scale": "threshold",
      "domain": ["1", "2", "3", "4"],
      "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
      "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
      "title": "Copy number"
    }
  }
}

The reads and their coverage

The tumor and normal BAMs carry no MD tags. Convert each to a local CRAM against the reference above and write a coverage bigWig beside it:

# -T names the reference the CRAM is written against, and it has to be the same
# build the assembly was loaded from or every base reads as a mismatch
# --write-index saves a second samtools pass for the .crai
samtools view HG008-T.bam --write-index -o HG008-T.cram -T GRCh38.fa

# one whole-genome coverage bigWig per sample, written beside its CRAM
megadepth HG008-T.cram --bigwig

Structural variants from the published callsets

The benchmark is one of five somatic SV callsets C-GIAB publishes for this pair, each loaded the same way:

Callset	Called from
NIST V0.5 draft benchmark	assembly comparison plus read support
Severus	PacBio HiFi
minda ensemble	eleven caller runs over HiFi, ONT and Illumina
DRAGEN	Illumina WGS
NYGC somatic pipeline (Manta and GRIDSS)	Illumina WGS

The chr3 breakends of the benchmark's cluster_3 in five SV callsets, over the HiFiCNV depth and the benchmark's CNV lane. Every callset marks both breakends, the depth steps down between them, and the CNV lane crosses the whole window as one segment. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

Severus and DRAGEN

Severus and DRAGEN are both indexed VCFs with a record at each breakend, so they load with no display settings:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_severus_sv",
  "name": "HG008-T Severus somatic SVs (HiFi)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi_Severus-SV_20240308/somatic_SVs/severus_somatic.vcf.gz"
  }
}

CLI (add-track)

jbrowse add-track https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi_Severus-SV_20240308/somatic_SVs/severus_somatic.vcf.gz \
  --trackId hg008t_severus_sv \
  --name "HG008-T Severus somatic SVs (HiFi)" \
  --assemblyNames GRCh38_GIABv3

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_severus_sv",
  "name": "HG008-T Severus somatic SVs (HiFi)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi_Severus-SV_20240308/somatic_SVs/severus_somatic.vcf.gz"
  }
}

Open in JBrowse

Open this track in JBrowse Web ↗

Open this track in JBrowse Desktop ↗ (JBrowse Desktop 5.0+)

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_dragen_sv",
  "name": "HG008-T DRAGEN somatic SVs (Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.sv.vcf.gz"
  }
}

CLI (add-track)

jbrowse add-track https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.sv.vcf.gz \
  --trackId hg008t_dragen_sv \
  --name "HG008-T DRAGEN somatic SVs (Illumina)" \
  --assemblyNames GRCh38_GIABv3

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_dragen_sv",
  "name": "HG008-T DRAGEN somatic SVs (Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.sv.vcf.gz"
  }
}

Open in JBrowse

Open this track in JBrowse Web ↗

Open this track in JBrowse Desktop ↗ (JBrowse Desktop 5.0+)

minda: the caller runs behind each junction

The ensemble callset is a plain VCF, so VcfAdapter loads it whole, with no index:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_minda_sv",
  "name": "HG008-T minda ensemble SVs (HiFi, ONT, Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH-NCI_minda-ensemble_20240710/HG008_minda_ensemble.vcf"
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "hg008t_minda_sv",
  "name": "HG008-T minda ensemble SVs (HiFi, ONT, Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH-NCI_minda-ensemble_20240710/HG008_minda_ensemble.vcf"
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_minda_sv",
  "name": "HG008-T minda ensemble SVs (HiFi, ONT, Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH-NCI_minda-ensemble_20240710/HG008_minda_ensemble.vcf"
  }
}

Open in JBrowse

Open this track in JBrowse Web ↗

Open this track in JBrowse Desktop ↗ (JBrowse Desktop 5.0+)

NYGC: a BEDPE drawn as arcs

BedpeAdapter reads a paired-end BED whole, with no index, and serves it to a variant track:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_nygc_sv",
  "name": "HG008-T NYGC somatic SVs (Manta, GRIDSS)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedpeAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.sv.annotated.v7.somatic.high_confidence.final.bedpe"
  },
  "displays": [
    {
      "type": "LinearMarkDisplay",
      "displayId": "hg008t_nygc_sv-LinearMarkDisplay",
      "marks": [
        {
          "mark": "link",
          "encoding": { "size": 2 },
          "transform": [{ "type": "mate" }]
        }
      ]
    }
  ]
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "hg008t_nygc_sv",
  "name": "HG008-T NYGC somatic SVs (Manta, GRIDSS)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedpeAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.sv.annotated.v7.somatic.high_confidence.final.bedpe"
  },
  "displays": [
    {
      "type": "LinearMarkDisplay",
      "displayId": "hg008t_nygc_sv-LinearMarkDisplay",
      "marks": [
        {
          "mark": "link",
          "encoding": { "size": 2 },
          "transform": [{ "type": "mate" }]
        }
      ]
    }
  ]
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_nygc_sv",
  "name": "HG008-T NYGC somatic SVs (Manta, GRIDSS)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedpeAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.sv.annotated.v7.somatic.high_confidence.final.bedpe"
  },
  "displays": [
    {
      "type": "LinearMarkDisplay",
      "displayId": "hg008t_nygc_sv-LinearMarkDisplay",
      "marks": [
        {
          "mark": "link",
          "encoding": { "size": 2 },
          "transform": [{ "type": "mate" }]
        }
      ]
    }
  ]
}

A link whose mate lies outside the view draws as a short stem at its own end, which is how both NYGC records at cluster_3 appear in the figure above.

Copy number from the published callsets

Four groups have called copy number on this pair, and C-GIAB publishes each one's output:

Callset	Called from	Each segment carries
NIST V0.5 draft benchmark	assembly comparison plus read support	absolute total and per-haplotype copy number
Wakhan	PacBio HiFi, phased with Hi-C	copy number per parental haplotype, with LOH intervals in a second file
NYGC somatic pipeline, BIC-seq2	Illumina WGS	log2 tumor-versus-normal copy ratio, and the genes the segment covers
DRAGEN	Illumina WGS	integer copy number, minor-haplotype copy number and minor allele frequency

The benchmark CNV BED added above is the lane the others get read against: its copy numbers are absolute, and CN 2 states a diploid region explicitly. Depth per bin is the one signal no group publishes, and the end of this section builds it from the tumor reads.

Four published CNV callsets over chr9p21.3, with the HiFiCNV depth above them. Depth drops out over CDKN2A, where the benchmark and NYGC both carry a focal call and the two coarser segmentations run straight through. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

DRAGEN: integer copy number from short reads

DRAGEN's CNV calls load as an indexed VCF, one record per segment. Each record's ID reads DRAGEN:CNLOH:chr9:22631070-22939213, so the label takes the class from its second field:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "hg008t_dragen_cnv",
  "name": "HG008-T DRAGEN somatic CNV (Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.cnv.vcf.gz"
  },
  "displayDefaults": {
    "labels": { "name": "jexl:split(feature.name,':')[1]" }
  }
}

CLI (add-track)

jbrowse add-track https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.cnv.vcf.gz \
  --trackId hg008t_dragen_cnv \
  --name "HG008-T DRAGEN somatic CNV (Illumina)" \
  --assemblyNames GRCh38_GIABv3 \
  --displayDefaults "{\"labels\":{\"name\":\"jexl:split(feature.name,':')[1]\"}}"

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "hg008t_dragen_cnv",
  "name": "HG008-T DRAGEN somatic CNV (Illumina)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/DRAGEN-v4.2.4_ILMN-WGS_20240312/standard/dragen_4.2.4_HG008-mosaic_tumor.cnv.vcf.gz"
  },
  "displayDefaults": {
    "labels": { "name": "jexl:split(feature.name,':')[1]" }
  }
}

NYGC: a copy ratio, and the genes each segment covers

C-GIAB publishes NYGC's CNV output two ways. HG008-T--HG008-N.cnv.annotated.v7.final.bed needs nothing done to it; its # header line lets the adapter read column names straight from the file.

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "hg008t_nygc_cnv",
  "name": "HG008-T NYGC CNV calls, annotated (BIC-seq2)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.cnv.annotated.v7.final.bed"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "color": {
      "field": "type",
      "domain": ["DEL", "DUP"],
      "range": ["#2166ac", "#b2182b"],
      "labels": ["Loss (DEL)", "Gain (DUP)"],
      "title": "Call"
    },
    "labels": { "name": "jexl:feature.type+' '+feature.cytoband" }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "hg008t_nygc_cnv",
  "name": "HG008-T NYGC CNV calls, annotated (BIC-seq2)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.cnv.annotated.v7.final.bed"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "color": {
      "field": "type",
      "domain": ["DEL", "DUP"],
      "range": ["#2166ac", "#b2182b"],
      "labels": ["Loss (DEL)", "Gain (DUP)"],
      "title": "Call"
    },
    "labels": { "name": "jexl:feature.type+'\'' '\''+feature.cytoband" }
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "hg008t_nygc_cnv",
  "name": "HG008-T NYGC CNV calls, annotated (BIC-seq2)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NYGC-somatic-pipeline_20240412/GRCh38-GIABv3/HG008-T--HG008-N.cnv.annotated.v7.final.bed"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "color": {
      "field": "type",
      "domain": ["DEL", "DUP"],
      "range": ["#2166ac", "#b2182b"],
      "labels": ["Loss (DEL)", "Gain (DUP)"],
      "title": "Call"
    },
    "labels": { "name": "jexl:feature.type+' '+feature.cytoband" }
  }
}

HG008-T--HG008-N.bicseq2.txt is the same segmentation in quantitative form, one log2 ratio per segment, and one awk away from a bedGraph:

# column 9 is log2.copyRatio, and the file is 1-based where bedGraph is not
awk 'NR>1 {printf "%s\t%d\t%d\t%.4f\n", $1, $2-1, $3, $9}' \
  HG008-T--HG008-N.bicseq2.txt > HG008-T_bicseq2_log2ratio.bedgraph

We'll plot it as bars from zero on a fixed axis, so a step means the same thing from one window to the next. A homozygous deletion has no reads and so no finite ratio, and the balanced baseline sits above zero.

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "QuantitativeTrack",
  "trackId": "hg008t_bicseq2",
  "name": "HG008-T copy ratio, segmented (NYGC BIC-seq2, log2 T/N)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedGraphAdapter",
    "uri": "HG008-T_bicseq2_log2ratio.bedgraph"
  },
  "displayDefaults": {
    "mark": "bar",
    "scales": {
      "y": {
        "domainMin": -2,
        "domainMax": 2,
        "grid": true,
        "title": "log2 tumor/normal"
      }
    }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "QuantitativeTrack",
  "trackId": "hg008t_bicseq2",
  "name": "HG008-T copy ratio, segmented (NYGC BIC-seq2, log2 T/N)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedGraphAdapter",
    "uri": "HG008-T_bicseq2_log2ratio.bedgraph"
  },
  "displayDefaults": {
    "mark": "bar",
    "scales": {
      "y": {
        "domainMin": -2,
        "domainMax": 2,
        "grid": true,
        "title": "log2 tumor/normal"
      }
    }
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "QuantitativeTrack",
  "trackId": "hg008t_bicseq2",
  "name": "HG008-T copy ratio, segmented (NYGC BIC-seq2, log2 T/N)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedGraphAdapter",
    "uri": "HG008-T_bicseq2_log2ratio.bedgraph"
  },
  "displayDefaults": {
    "mark": "bar",
    "scales": {
      "y": {
        "domainMin": -2,
        "domainMax": 2,
        "grid": true,
        "title": "log2 tumor/normal"
      }
    }
  }
}

HG008-T_bicseq2_log2ratio.bedgraph is relative to a config.json. Replace it with its URL or its path on this computer.

Wakhan: copy number per parental haplotype

HG008_HiFi_HiC_copynumbers_segments.bed is long format, one row per haplotype, with no # on its column-name line:

chr	start	end	copynumber_state	coverage	haplotype
chr1	0	23750000	2	106.025	1
chr1	23750001	119650000	0.72	58.025	1

Name the columns on the adapter with columnNames. Because the haplotype column already assigns each segment to a row, use LinearMultiRowFeatureDisplay and set rows to haplotype:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "hg008t_wakhan_hifi_hic",
  "name": "HG008-T Wakhan copy number per haplotype (HiFi + Hi-C)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi-HiC_Wakhan-CNA_20240424/bed_output/HG008_HiFi_HiC_copynumbers_segments.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "copynumber_state",
      "coverage",
      "haplotype"
    ]
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008t_wakhan_hifi_hic-LinearMultiRowFeatureDisplay",
      "rows": "haplotype",
      "color": {
        "field": "copynumber_state",
        "scale": "threshold",
        "domain": ["0.5", "1.5"],
        "range": ["#2166ac", "#bdbdbd", "#f4a582"],
        "labels": ["Haplotype lost (0)", "One copy", "Two or more copies"],
        "title": "Copy number per haplotype"
      }
    }
  ]
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "hg008t_wakhan_hifi_hic",
  "name": "HG008-T Wakhan copy number per haplotype (HiFi + Hi-C)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi-HiC_Wakhan-CNA_20240424/bed_output/HG008_HiFi_HiC_copynumbers_segments.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "copynumber_state",
      "coverage",
      "haplotype"
    ]
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008t_wakhan_hifi_hic-LinearMultiRowFeatureDisplay",
      "rows": "haplotype",
      "color": {
        "field": "copynumber_state",
        "scale": "threshold",
        "domain": ["0.5", "1.5"],
        "range": ["#2166ac", "#bdbdbd", "#f4a582"],
        "labels": ["Haplotype lost (0)", "One copy", "Two or more copies"],
        "title": "Copy number per haplotype"
      }
    }
  ]
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "hg008t_wakhan_hifi_hic",
  "name": "HG008-T Wakhan copy number per haplotype (HiFi + Hi-C)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedAdapter",
    "uri": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/HG008/Liss_lab/analysis/NIH_HiFi-HiC_Wakhan-CNA_20240424/bed_output/HG008_HiFi_HiC_copynumbers_segments.bed",
    "columnNames": [
      "chrom",
      "start",
      "end",
      "copynumber_state",
      "coverage",
      "haplotype"
    ]
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008t_wakhan_hifi_hic-LinearMultiRowFeatureDisplay",
      "rows": "haplotype",
      "color": {
        "field": "copynumber_state",
        "scale": "threshold",
        "domain": ["0.5", "1.5"],
        "range": ["#2166ac", "#bdbdbd", "#f4a582"],
        "labels": ["Haplotype lost (0)", "One copy", "Two or more copies"],
        "title": "Copy number per haplotype"
      }
    }
  ]
}

copynumber_state of 0 is a lost haplotype behind an arm LOH; 1 is expected.

Depth per bin, and B-allele frequency

HiFiCNV writes a binned depth track from the tumor reads:

# --maf holds the TUMOR's small-variant calls, the Clair3 tumor VCF published
# alongside C-GIAB's Wakhan run: HiFiCNV reads AD out of this VCF for its
# allele-frequency output; --bam supplies depth only
hificnv --bam HG008-T.cram --ref GRCh38.fa --maf tumor_smallvariants.vcf.gz \
  --output-prefix hificnv

HiFiCNV names its depth output for the --bam sample. Give it the Scatter plot type.

The allelic panel is B-allele frequency, unfolded: a balanced region is one band at 0.5, a loss-of-heterozygosity region splits into two bands at 0 and 1. Build it by piling up the tumor reads at the sites the normal calls heterozygous and taking the alt fraction:

# het sites from the NORMAL: an LOH site is homozygous in the tumor, so a
# tumor-derived list would drop exactly the sites this track needs to show
bcftools view -g het -Oz -o hets.vcf.gz normal.deepvariant.vcf.gz
tabix -p vcf hets.vcf.gz
cut -f1,2 GRCh38.fa.fai > GRCh38.chrom.sizes

# -q 1 drops multi-mapped reads, -Q 0 leaves HiFi base qualities alone
bcftools mpileup -f GRCh38.fa -T hets.vcf.gz -a AD -q 1 -Q 0 tumor.bam |
  bcftools query -f '%CHROM\t%POS\t[%AD]\n' |
  # unfolded alt fraction, so LOH separates into bands at 0 and 1; folding
  # would merge them into one. The 10x floor keeps thin coverage from painting a fake 0/1
  awk -F'[\t,]' '{d=$3+$4; if (d>=10) printf "%s\t%d\t%d\t%.4f\n",$1,$2-1,$2,$4/d}' |
  LC_COLLATE=C sort -k1,1 -k2,2n > baf.bedgraph
bedGraphToBigWig baf.bedgraph GRCh38.chrom.sizes tumor_baf.bw

Plot it with Scatter over a fixed 0 to 1 range.

Chromosome 3 over the benchmark CNV calls: BIC-seq2's segmented log2 copy ratio, the HiFiCNV depth, and B-allele frequency. The p-arm is a single-copy loss with loss-of-heterozygosity; the q-arm is balanced. Open this view in JBrowse ↗

Keep the BAF track off bigWig summaries

A bigWig's default zoomed-out summary paints an LOH arm as a solid full-height wash. A small resolutionMultiplier keeps the fetch on raw per-site values at these figures' zoom levels:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "QuantitativeTrack",
  "trackId": "HG008-T_baf",
  "name": "HG008-T B-allele frequency (BAF)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigWigAdapter",
    "uri": "HG008-T_baf.bcftools.bw",
    "resolutionMultiplier": 0.001
  },
  "displayDefaults": {
    "mark": "point",
    "size": 1,
    "scales": { "y": { "domainMin": 0, "domainMax": 1 } }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "QuantitativeTrack",
  "trackId": "HG008-T_baf",
  "name": "HG008-T B-allele frequency (BAF)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigWigAdapter",
    "uri": "HG008-T_baf.bcftools.bw",
    "resolutionMultiplier": 0.001
  },
  "displayDefaults": {
    "mark": "point",
    "size": 1,
    "scales": { "y": { "domainMin": 0, "domainMax": 1 } }
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "QuantitativeTrack",
  "trackId": "HG008-T_baf",
  "name": "HG008-T B-allele frequency (BAF)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigWigAdapter",
    "uri": "HG008-T_baf.bcftools.bw",
    "resolutionMultiplier": 0.001
  },
  "displayDefaults": {
    "mark": "point",
    "size": 1,
    "scales": { "y": { "domainMin": 0, "domainMax": 1 } }
  }
}

HG008-T_baf.bcftools.bw is relative to a config.json. Replace it with its URL or its path on this computer.

Subclonal copy number

HG008-T is a cellular mixture: a genome-doubled fraction grows across passages (Wagner et al. 2026), and the benchmark CNV BED reports copy number for the cells that have not doubled.

C-GIAB publishes short-read WGS for a panel of HG008-T single-cell-derived clones under HG008-T_clones/. Merged per clone into one BED with a clone column, they partition into rows like the Wakhan haplotypes; a row that departs from the rest is a CNV private to that subclone:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "hg008_subclonal_cnv",
  "name": "HG008-T subclonal CNV (per-clone CNVkit)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedTabixAdapter",
    "uri": "https://jbrowse.org/demos/cgiab/HG008T-clones.cnv.multirow.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008_subclonal_cnv-LinearMultiRowFeatureDisplay",
      "rows": "clone",
      "color": {
        "field": "cn",
        "scale": "threshold",
        "domain": ["1", "2", "3", "4"],
        "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
        "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
        "title": "Copy number"
      }
    }
  ]
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "hg008_subclonal_cnv",
  "name": "HG008-T subclonal CNV (per-clone CNVkit)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedTabixAdapter",
    "uri": "https://jbrowse.org/demos/cgiab/HG008T-clones.cnv.multirow.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008_subclonal_cnv-LinearMultiRowFeatureDisplay",
      "rows": "clone",
      "color": {
        "field": "cn",
        "scale": "threshold",
        "domain": ["1", "2", "3", "4"],
        "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
        "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
        "title": "Copy number"
      }
    }
  ]
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "hg008_subclonal_cnv",
  "name": "HG008-T subclonal CNV (per-clone CNVkit)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BedTabixAdapter",
    "uri": "https://jbrowse.org/demos/cgiab/HG008T-clones.cnv.multirow.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "displayId": "hg008_subclonal_cnv-LinearMultiRowFeatureDisplay",
      "rows": "clone",
      "color": {
        "field": "cn",
        "scale": "threshold",
        "domain": ["1", "2", "3", "4"],
        "range": ["#2166ac", "#92c5de", "#e0e0e0", "#f4a582", "#b2182b"],
        "labels": ["CN 0", "CN 1", "CN 2", "CN 3", "CN 4+"],
        "title": "Copy number"
      }
    }
  ]
}

Read those integers on the caller's own scale: CNVkit centers each sample's log2 on its own median, so the balanced state here is not CN 2. The benchmark CNV track anchors them with an absolute total_copy_number.

Align the tumor assembly to GRCh38

The tumor assembly is haplotype-resolved into T2T scaffolds. Load it as a second JBrowse assembly and align it to GRCh38; the synteny and dotplot views draw from the resulting PAF:

# asm5 is the same-species preset; -c emits the base-level CIGAR the synteny
# view needs to draw a junction at base scale
minimap2 -cx asm5 GRCh38.fa HG008T_v3.2.fasta > HG008T_v3.2.paf

# -a is query,target, the REVERSE of the target query minimap2 just took: get it
# backwards and the view opens empty with no error
jbrowse add-track HG008T_v3.2.paf -a HG008T_v3.2,GRCh38_GIABv3

The matched normal assembly (HG008N_v6.3.fasta.gz, same S3 path) loads the same way. See the synteny track config guide.

Walkthroughs

Each walkthrough runs on an instance built above, or on the hosted C-GIAB demo, which already carries the benchmark calls, the reads and the copy-number tracks.

A chr3-chr13 translocation

Add → SV inspector, then Open from track to pick the C-GIAB benchmark VCF loaded earlier.

The SV inspector showing the benchmark VCF as a circular overview alongside a table of calls. Open this view in JBrowse ↗

Click the chord joining chr3 and chr13, then open the tumor PacBio HiFi reads on each panel of the breakpoint split view it launches and set Read height → Compact.

Clicking the chord joining chr3 and chr13 opens a breakpoint split view. Splines connect tumor PacBio HiFi reads that partially map to each chromosome, evidence of a fusion or translocation. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

The next walkthrough reads this one three ways.

The same junction three ways

SV_20 and SV_190 are one junction written twice, joining chr3:139,976,414 to chr13:114,353,244, filed under EVENT=cluster_3 with two further breakends and tagged EVENTTYPE=CHROMOPLEXY. Choose cluster_3 under Filter by event, and open a record in a breakpoint split view for a panel at each locus.

In that view, splines join each tumor PacBio HiFi read's chr13 piece to its chr3 piece: chr13 forward into the junction, then down chr3 inverted. The matched normal reads through the same locus with no split.

The synteny track loaded earlier shows the same junction with no reads: the C-GIAB assembly resolves both loci onto one tumor contig, named for the two chromosomes it fuses.

The hosted demo slices the tumor reads to the loci these walkthroughs visit, reaching one of cluster_3's junctions. The build script lifts that limit.

Which calls are drivers

In pancreatic ductal adenocarcinoma the recurrently altered genes are KRAS, CDKN2A, TP53 and SMAD4 (Waddell et al. 2015, Bailey et al. 2016); every copy-number figure below draws one MANE Select transcript under the lanes.

A small deletion in CUZD1

Use the search (magnifying glass) button in the SV inspector for SV_85, a heterozygous deletion affecting two exons of CUZD1 (NCBI Gene 50624) that takes one of its two copies; at ~1.8 kb it reads base by base in a pileup.

ClinVar CNVs carries submitted copy-number variants and their clinical significance, served by UCSC as a bigBed:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "hg38_clinvar_cnv_ucsc",
  "name": "ClinVar CNVs (UCSC)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigBedAdapter",
    "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinvar/clinvarCnv.bb"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "filter": ["jexl:get(feature,'_varLen') < 50000"]
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "hg38_clinvar_cnv_ucsc",
  "name": "ClinVar CNVs (UCSC)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigBedAdapter",
    "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinvar/clinvarCnv.bb"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "filter": ["jexl:get(feature,'\''_varLen'\'') < 50000"]
  }
}'

JBrowse Desktop

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "hg38_clinvar_cnv_ucsc",
  "name": "ClinVar CNVs (UCSC)",
  "assemblyNames": ["GRCh38_GIABv3"],
  "adapter": {
    "type": "BigBedAdapter",
    "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinvar/clinvarCnv.bb"
  },
  "displays": [{ "type": "LinearBasicDisplay", "displayMode": "compact" }],
  "displayDefaults": {
    "filter": ["jexl:get(feature,'_varLen') < 50000"]
  }
}

The size filter keeps the lane at this event's scale. No submitted CNV near this deletion's size covers this locus.

The SV inspector after searching for SV_85, a heterozygous CUZD1 deletion, and the linear genome view its location link opens: the <DEL> ALT allele over the ClinVar CNV and NCBI RefSeq gene lanes. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

Open the gene annotations and the tumor PacBio HiFi reads, set Read height → Compact and Sort by... → Base pair from the track menu, and center the deletion. The view menu's center line helps line up the breakpoint.

Tumor PacBio HiFi reads at compact height, sorted by base pair with the deletion centered, over the gene annotations. The deletion removes two CUZD1 exons and is heterozygous. Open this view in JBrowse ↗

A tandem-repeat call, sized against the normal

Some benchmark records, tagged EVENTTYPE=CNV:TR, size a somatic change inside a tandem repeat against the donor's own germline allele, giving SVLEN a value specific to this pair.

SV_223 on chr5 is the benchmark's own worked example. Open it with both samples' PacBio HiFi reads and sort each pileup at the call:

SV_223 at base level: the benchmark's deletion call over the tumor and matched normal PacBio HiFi pileups. The tumor's reads carry a deletion where the normal's carry an insertion at the same repeat, and the called span is wider than the deletion under it. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

The record is also given in the normal assembly's coordinates (CHROM_HG8N6.3 and its siblings), so clicking it shows the same variant against the normal's own sequence.

Reading copy number

The quickest copy-number check is the tumor and normal coverage bigWigs as one multi-bigwig track:

Show all regions in assembly on the linear genome view start screen opens every chromosome at once.
Score → Set min/max score... in the track menu pins the axis off the centromere and repeat spikes.
Plot type → Overlapping → Scatter draws the two samples as points in one band, tumor red and normal blue.

The linear genome view start screen, with every chromosome laid out across the view. Open this view in JBrowse ↗

The tumor and normal rows come from goleft indexcov, published as HG008-N_indexcov.bw and HG008-T_indexcov.bw, and load as a multi-wiggle track by URL.

Zoom to a region and open the benchmark CNV BED against the called intervals: coverage marks that a level changed, and the BAF track shows what changed.

Plot type → Overlapping → Scatter on the coverage track over chr5, redrawing the two stacked rows as one band of points: the normal holds flat while the tumor steps. Open the session this video starts in ↗

Video

Open the session the video starts in ↗, then take the steps it shows:

Tumor and normal on stacked rows, each autoscaled on its own
Overlay the two rows as points on one axis
The tumor sits above its normal on 5p, below it on 5q

Desktop

Open this view in JBrowse Desktop ↗

Opens JBrowse Desktop (JBrowse Desktop 5.0+) at this view and saves it as a reopenable session.

Nothing happens?

Paste this link into Desktop's Open JBrowse Web link... (start screen, or File → Session):

https://jbrowse.org/code/jb2/main/?config=https%3A%2F%2Fjbrowse.org%2Fdemos%2Fcgiab%2Fconfig.json&session=spec-%7B%22sessionTracks%22%3A%5B%7B%22type%22%3A%22MultiQuantitativeTrack%22%2C%22trackId%22%3A%22hg008_cnv_indexcov%22%2C%22name%22%3A%22HG008+normal+vs+tumor+coverage+%28indexcov%29%22%2C%22assemblyNames%22%3A%5B%22GRCh38_GIABv3%22%5D%2C%22adapter%22%3A%7B%22type%22%3A%22MultiWiggleAdapter%22%2C%22subadapters%22%3A%5B%7B%22name%22%3A%22HG008-N+%28normal%29%22%2C%22type%22%3A%22BigWigAdapter%22%2C%22bigWigLocation%22%3A%7B%22uri%22%3A%22https%3A%2F%2Fjbrowse.org%2Fdemos%2Fcgiab%2FHG008-N_indexcov.bw%22%2C%22locationType%22%3A%22UriLocation%22%7D%7D%2C%7B%22name%22%3A%22HG008-T+%28tumor%29%22%2C%22type%22%3A%22BigWigAdapter%22%2C%22bigWigLocation%22%3A%7B%22uri%22%3A%22https%3A%2F%2Fjbrowse.org%2Fdemos%2Fcgiab%2FHG008-T_indexcov.bw%22%2C%22locationType%22%3A%22UriLocation%22%7D%7D%5D%7D%7D%5D%2C%22views%22%3A%5B%7B%22type%22%3A%22LinearGenomeView%22%2C%22assembly%22%3A%22GRCh38_GIABv3%22%2C%22loc%22%3A%22chr5%22%2C%22tracks%22%3A%5B%7B%22trackId%22%3A%22hg008_cnv_indexcov%22%2C%22type%22%3A%22LinearWiggleDisplay%22%2C%22height%22%3A200%7D%5D%7D%5D%7D&sessionName=JBrowse+docs%3A+sv_cgiab%2Fcopy_number_layout

With your own data, the steps behind the figure:

Open your genome: on the JBrowse Desktop start screen click Open new genome (or Show all available genomes to pick a hosted one).This figure uses GRCh38_GIABv3, loaded from a bgzip-compressed FASTA (.fa.gz + .fai + .gzi).
Add your own track: File → Open track..., then paste a URL or choose a local file.This figure uses “HG008 normal vs tumor coverage (indexcov)”.
1. Drag the bar at the bottom edge of the track to resize it (200px here).
Type your region of interest into the location box and press Enter.This figure is at chr5.

Web

Open this view in JBrowse Web ↗

Its genome and tracks are hosted, so File → Open track... there adds your own files beside them.

With your own data, the steps behind the figure:

Put your genome in your JBrowse Web's config.json: jbrowse add-assembly adds it.This figure uses GRCh38_GIABv3, loaded from a bgzip-compressed FASTA (.fa.gz + .fai + .gzi).
Add your own track: File → Open track..., then paste a URL or choose a local file.This figure uses “HG008 normal vs tumor coverage (indexcov)”.
1. Drag the bar at the bottom edge of the track to resize it (200px here).
Type your region of interest into the location box and press Enter.This figure is at chr5.

CLI

A figure adds its tracks to one session. The jbrowse CLI writes the same tracks into a config.json instead, where every session that opens it has them: add-track where flags cover the whole track, add-track-json where they do not.

jbrowse add-track-json '{
  "type": "MultiQuantitativeTrack",
  "trackId": "hg008_cnv_indexcov",
  "name": "HG008 normal vs tumor coverage (indexcov)",
  "assemblyNames": [
    "GRCh38_GIABv3"
  ],
  "adapter": {
    "type": "MultiWiggleAdapter",
    "subadapters": [
      {
        "name": "HG008-N (normal)",
        "type": "BigWigAdapter",
        "bigWigLocation": {
          "uri": "https://jbrowse.org/demos/cgiab/HG008-N_indexcov.bw",
          "locationType": "UriLocation"
        }
      },
      {
        "name": "HG008-T (tumor)",
        "type": "BigWigAdapter",
        "bigWigLocation": {
          "uri": "https://jbrowse.org/demos/cgiab/HG008-T_indexcov.bw",
          "locationType": "UriLocation"
        }
      }
    ]
  }
}'

Run these where the config.json is, or add --out <dir>, and point each uri at your own file. The config needs GRCh38_GIABv3 already — jbrowse add-assembly adds yours. The location and the settings the steps carry are session state rather than track config — that half is the Spec tab, or jbrowse set-default-session.

Spec

This session spec draws the figure from https://jbrowse.org/demos/cgiab/config.json: paste it after &session=spec- on a JBrowse Web link that loads that config.

{
  "sessionTracks": [
    {
      "type": "MultiQuantitativeTrack",
      "trackId": "hg008_cnv_indexcov",
      "name": "HG008 normal vs tumor coverage (indexcov)",
      "assemblyNames": [
        "GRCh38_GIABv3"
      ],
      "adapter": {
        "type": "MultiWiggleAdapter",
        "subadapters": [
          {
            "name": "HG008-N (normal)",
            "type": "BigWigAdapter",
            "bigWigLocation": {
              "uri": "https://jbrowse.org/demos/cgiab/HG008-N_indexcov.bw",
              "locationType": "UriLocation"
            }
          },
          {
            "name": "HG008-T (tumor)",
            "type": "BigWigAdapter",
            "bigWigLocation": {
              "uri": "https://jbrowse.org/demos/cgiab/HG008-T_indexcov.bw",
              "locationType": "UriLocation"
            }
          }
        ]
      }
    }
  ],
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "GRCh38_GIABv3",
      "loc": "chr5",
      "tracks": [
        {
          "trackId": "hg008_cnv_indexcov",
          "type": "LinearWiggleDisplay",
          "height": 200
        }
      ]
    }
  ]
}

Notebook

The same view with jbrowse-anywidget, from the config the figure loads. Swap a track's uri for your own file.

from jbrowse_anywidget import JBrowseApp, fetch_hub

config = fetch_hub("https://jbrowse.org/demos/cgiab/config.json")
app = JBrowseApp(
    assemblies=config["assemblies"],
    tracks=config.get("tracks", []) + [
        {
            "type": "MultiQuantitativeTrack",
            "trackId": "hg008_cnv_indexcov",
            "name": "HG008 normal vs tumor coverage (indexcov)",
            "assemblyNames": [
                "GRCh38_GIABv3"
            ],
            "adapter": {
                "type": "MultiWiggleAdapter",
                "subadapters": [
                    {
                        "name": "HG008-N (normal)",
                        "type": "BigWigAdapter",
                        "bigWigLocation": {
                            "uri": "https://jbrowse.org/demos/cgiab/HG008-N_indexcov.bw",
                            "locationType": "UriLocation"
                        }
                    },
                    {
                        "name": "HG008-T (tumor)",
                        "type": "BigWigAdapter",
                        "bigWigLocation": {
                            "uri": "https://jbrowse.org/demos/cgiab/HG008-T_indexcov.bw",
                            "locationType": "UriLocation"
                        }
                    }
                ]
            }
        }
    ],
    views=[
        {
            "type": "LinearGenomeView",
            "assembly": "GRCh38_GIABv3",
            "loc": "chr5",
            "tracks": [
                {
                    "trackId": "hg008_cnv_indexcov",
                    "type": "LinearWiggleDisplay",
                    "height": 200
                }
            ]
        }
    ],
)
app  # display the widget

Image

jbrowse-img draws the same view as an SVG, from the command line and without a browser. Point --config at your own config, or swap a track for your own file with --bam, --vcfgz and the other file flags.

cat > tracks.json <<'JSON'
[
  {
    "type": "MultiQuantitativeTrack",
    "trackId": "hg008_cnv_indexcov",
    "name": "HG008 normal vs tumor coverage (indexcov)",
    "assemblyNames": [
      "GRCh38_GIABv3"
    ],
    "adapter": {
      "type": "MultiWiggleAdapter",
      "subadapters": [
        {
          "name": "HG008-N (normal)",
          "type": "BigWigAdapter",
          "bigWigLocation": {
            "uri": "https://jbrowse.org/demos/cgiab/HG008-N_indexcov.bw",
            "locationType": "UriLocation"
          }
        },
        {
          "name": "HG008-T (tumor)",
          "type": "BigWigAdapter",
          "bigWigLocation": {
            "uri": "https://jbrowse.org/demos/cgiab/HG008-T_indexcov.bw",
            "locationType": "UriLocation"
          }
        }
      ]
    }
  }
]
JSON

npx @jbrowse/img --config https://jbrowse.org/demos/cgiab/config.json \
  --tracks tracks.json \
  --assembly GRCh38_GIABv3 --loc chr5 \
  --track hg008_cnv_indexcov display:LinearWiggleDisplay '{"height":200}' \
  --width 1920 --out figure.svg

--out figure.png writes a PNG instead, through rsvg-convert (apt install librsvg2-bin, brew install librsvg).

Agent

Hand this to a coding agent. It rebuilds the figure above headlessly, and the session file is then the thing to edit — swap an adapter uri for your own file and rerun.

cat > session.json <<'JSON'
{
  "sessionTracks": [
    {
      "type": "MultiQuantitativeTrack",
      "trackId": "hg008_cnv_indexcov",
      "name": "HG008 normal vs tumor coverage (indexcov)",
      "assemblyNames": [
        "GRCh38_GIABv3"
      ],
      "adapter": {
        "type": "MultiWiggleAdapter",
        "subadapters": [
          {
            "name": "HG008-N (normal)",
            "type": "BigWigAdapter",
            "bigWigLocation": {
              "uri": "https://jbrowse.org/demos/cgiab/HG008-N_indexcov.bw",
              "locationType": "UriLocation"
            }
          },
          {
            "name": "HG008-T (tumor)",
            "type": "BigWigAdapter",
            "bigWigLocation": {
              "uri": "https://jbrowse.org/demos/cgiab/HG008-T_indexcov.bw",
              "locationType": "UriLocation"
            }
          }
        ]
      }
    }
  ],
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "GRCh38_GIABv3",
      "loc": "chr5",
      "tracks": [
        {
          "trackId": "hg008_cnv_indexcov",
          "type": "LinearWiggleDisplay",
          "height": 200
        }
      ]
    }
  ]
}
JSON

npx @jbrowse/capture --instance https://jbrowse.org/code/jb2/main/ \
  --config https://jbrowse.org/demos/cgiab/config.json \
  --width 1920 --height 520 \
  --session session.json -o figure.png

Using JBrowse with AI agents covers the rest: hosted genomes to point it at, and why waiting for the render is the part that goes wrong.

Chromosome 5: the segmented copy ratio, tumor and normal indexcov coverage as overlapping scatter, B-allele frequency, and the benchmark CNV calls. The normal stays flat while the tumor steps, and the BAF lane shows what each step is. Open this view in JBrowse ↗

Four loci in HG008-T sit in four different copy-number states, and the depth, BAF and copy-number lanes built above tell them apart:

Locus	State in HG008-T	Signature on the tracks
CDKN2A	Focal homozygous deletion (CN 0)	depth to 0, copy number 0
TP53	17p loss + LOH (CN 1, 1+0)	depth halved, BAF splits to 0 and 1
SMAD4	18q loss + LOH (CN 1, 0+1)	depth halved, BAF splits to 0 and 1
KRAS	Tandem duplication (CN 3, 2+1)	depth raised, BAF to 1/3 and 2/3

Arm-level loss is widespread here, so a single band at 0.5 is the exception; chr17 below is LOH end to end.

CDKN2A: a homozygous deletion inside a single-copy loss

Navigate to CDKN2A on chr9: the benchmark calls a focal ~20 kb homozygous deletion over the gene (SV_75, CN 0), inside a larger single-copy-loss arm (CNA_14, 0+1) where depth is already halved (Wagner et al. 2026).

Load the tumor and matched normal per-base coverage as one multi-quantitative track, one row per sample, with an explicit score range. Thin lines crossing the read pileup's gap are single reads carrying the deletion.

The benchmark's total_copy_number is absolute: CN 2 is diploid, and 9p has already lost a copy, so CN 1 is the local background. Widen the view several hundred kilobases right to read CN 2 against it.

The CDKN2A deletion at 60 kb: coverage drops out in the tumor row and not in the normal, the read pileup drops out with it, and the CNV call under them reads CN 0. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

chr17: loss with LOH, and copy-neutral LOH

Chromosome 17 carries a different LOH state on each arm. Open the whole chromosome with the depth track above the BAF:

the p-arm (covering TP53) is a single-copy loss with LOH (CNA_20, CN 1, 1+0): depth is halved and the BAF splits away from 0.5.
the q-arm is copy-neutral LOH (CNA_21, CN 2, 2+0): one haplotype lost, the other duplicated, so depth stays flat but the BAF still splits away from 0.5.

The copy-ratio lane fills from a zero pivot, above which the q-arm's copy-neutral state fills upward like a gain. Read the lane by its steps.

Chromosome 17: the segmented copy ratio, the HiFiCNV depth, the BAF and the benchmark CNV calls. The p-arm is a single-copy loss with LOH; the q-arm is copy-neutral LOH, flat in both copy-number lanes and still split in the BAF. Open this view in JBrowse ↗

The depth and BAF combinations read as a compact decision table:

depth	BAF	Interpretation
flat (CN 2)	one band at 0.5	balanced diploid
flat (CN 2)	split to 0, 1	copy-neutral LOH
halved	split to 0, 1	single-copy loss with LOH
raised	1/3 and 2/3	allelic gain

The benchmark BED's hap1_copy_number/hap2_copy_number columns encode this: a 0 haplotype (e.g. 1+0, 2+0) has lost one parental allele, splitting the BAF away from 0.5 regardless of total copy number. Clicking a CNV feature shows both.

KRAS and SMAD4

KRAS on chr12 sits in a gain (SV_101, CN 3, 2+1), a 2 Mb tandem duplication carrying the G12V-mutated copy (Wagner et al. 2026), a handful of pixels wide at whole-chromosome scale.

KRAS on chr12: its MANE Select transcript over the segmented copy ratio, the HiFiCNV depth and the BAF, above the CNV calls. Over the tandem duplication the copy-ratio edges land on the called boundaries and the BAF separates into two bands. Open this view in JBrowse ↗

SMAD4 on 18q is lost with LOH (CNA_48, CN 1, 0+1), the mirror image of TP53, with two controls: the balanced p-arm and the matched normal. Leave the display's bicolor mode on with a symmetric axis, so a step down and a step up fill equally.

Chromosome 18: SMAD4's MANE Select transcript over the segmented copy ratio, the tumor and its matched normal from indexcov, and the BAF, above the CNV calls. All three lanes change together from ~30 Mb to the telomere. Open this view in JBrowse ↗

Synteny and dotplot views of the tumor assembly

Add → Dotplot view, set the de novo assembly as one axis and GRCh38 as the other, and pick the matching synteny track.

The dotplot import form, with the HG008-T v3.2 assembly on one axis and GRCh38 on the other. Open this view in JBrowse ↗

HG008-T v3.2's scaffold names end in _hap1 or _hap2, so one plot stacks both haplotypes and doubles every diagonal. Restrict the y axis to one haplotype at a time for a plain assembly-vs-reference diagonal.

The two haplotypes of HG008-T v3.2 (y) against GRCh38 chromosomes (x), hap1 left and hap2 right. Each scaffold is one diagonal segment. On hap1 the chr3_chr13_hap1 scaffold carries a piece of chr3 and a piece of chr13, the translocation; on hap2 chr13_hap2 is one unbroken diagonal against chr13. Open this view in JBrowse ↗

Drag over a region and take Launch → Linear synteny view, keeping HG008T v3.2 as the synteny dataset, then enter chr3 chr13 in the GRCh38 search box. Raising the minimum alignment length drops short, noisy anchors; zooming into a breakpoint reads it at base level.

Add row in the import form gives each haplotype a separate panel: hap2, GRCh38, hap1, one level of ribbons per adjacent pair. Three rows keep the fusion legible, since hap1's ribbons cross where hap2's do not.

A three-row synteny view of the chr3/chr13 selection: hap2 on top, GRCh38 chr3 and chr13 in the middle, and the fused chr3_chr13_hap1 scaffold below, at a raised minimum alignment length. The two blue bands on the reference row mark the translocation's breakends, chr3:139,976,414 and chr13:114,353,244. The ribbons below the reference cross between them, where hap1 joins the two chromosomes; the ribbons above run to hap2, whose chr13 is unrearranged and whose chr3 material sits in a scaffold fused with chr6 and chr11. Open this view in JBrowse ↗

A scaffold named for two GRCh38 chromosomes is the cue.

For more on these views, see the dotplot view guide and the linear synteny view guide.

Methylation on the tumor reads

The C-GIAB PacBio HiFi BAMs carry per-read 5mC calls in their MM/ML tags, rendered with no extra files. Open the tumor reads. Set Color by... → Modifications, then One color per type, plus low-probability & unmodified in blue, which paints every CpG in context.

Tumor PacBio HiFi reads at the CDKN2B-AS1 end of the CDKN2A locus, over the NCBI RefSeq gene lane, colored by base modification with unmodified cytosines filled in. Neighboring CpG-dense blocks come out in opposite states, one of them at the CDKN2B-AS1 transcription start. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

Where the marks thin out to scattered ticks, that is CpG density: the fill draws a cytosine only where the reference puts one in context.

See Modifications and methylation for the display modes, and the methylation tutorial for the aggregate and allele-specific views.

Where to go next

Swap the VCF, the CRAMs, the caller output and the assembly for your own; the same tracks and walkthroughs apply. See the SV visualization guide for further display options.

Within C-GIAB itself there is more on the same FTP than this tutorial loads:

a somatic small-variant draft benchmark, which loads as a variant track the same way
the matched normal assembly (HG008N), which loads as a second JBrowse assembly and serves as the synteny target
HG009, a second matched pair (PDAC liver metastasis with matched CD4+ T cells) on the NIST C-GIAB page

Reproduce it end to end

build_sv_visualization_cgiab.sh runs the whole data-preparation pipeline above in one shot:

curl -fO https://raw.githubusercontent.com/GMOD/jbrowse-components/main/scripts/build_sv_visualization_cgiab.sh
bash build_sv_visualization_cgiab.sh   # builds ./cgiab_build/jbrowse2
npx --yes serve cgiab_build/jbrowse2

The script fetches the C-GIAB GRCh38 build and the V0.5 benchmark calls, then runs the CRAM, coverage, HiFiCNV, BAF and minimap2 steps above, downloads JBrowse, and writes a config.json with all of it loaded beside the published Wakhan segments.

The script needs the tools listed under Prerequisites, plus bcftools and bedGraphToBigWig. It pulls down more than 200 GB, wants roughly 1.5 TB of free disk and 32 GB of RAM, and its alignment and copy-number steps take hours.

References

Bailey et al. (2016). Genomic analyses identify molecular subtypes of pancreatic cancer
Diesh et al. (2023). JBrowse 2: A Modular Genome Browser with Views of Synteny and Structural Variation
McDaniel et al. (2025). Development and Extensive Sequencing of a Broadly-Consented Genome in a Bottle Matched Tumor-Normal Pair
Rautiainen et al. (2023). Verkko: telomere-to-telomere assembly of diploid chromosomes
Waddell et al. (2015). Whole genomes redefine the mutational landscape of pancreatic cancer
Wagner et al. (2026). A complete human pancreatic cancer genome

Feedback on this tutorial is welcome: contact us.

Structural variants (Cancer GIAB)

Prerequisites#

Where the data comes from#

The C-GIAB dataset#

Setting up#

The benchmark SV and CNV calls#

The reads and their coverage#

Structural variants from the published callsets#

Severus and DRAGEN#

minda: the caller runs behind each junction#

NYGC: a BEDPE drawn as arcs#

Copy number from the published callsets#

DRAGEN: integer copy number from short reads#

NYGC: a copy ratio, and the genes each segment covers#

Wakhan: copy number per parental haplotype#

Depth per bin, and B-allele frequency#

Keep the BAF track off bigWig summaries#

Subclonal copy number#

Align the tumor assembly to GRCh38#

Walkthroughs#

A chr3-chr13 translocation#

The same junction three ways#

Which calls are drivers#

A small deletion in CUZD1#

A tandem-repeat call, sized against the normal#

Reading copy number#

CDKN2A: a homozygous deletion inside a single-copy loss#

chr17: loss with LOH, and copy-neutral LOH#

KRAS and SMAD4#

Synteny and dotplot views of the tumor assembly#

Methylation on the tumor reads#

Where to go next#

Reproduce it end to end#

See also#

References#

Prerequisites

Where the data comes from

The C-GIAB dataset

Setting up

The benchmark SV and CNV calls

The reads and their coverage

Structural variants from the published callsets

Severus and DRAGEN

minda: the caller runs behind each junction

NYGC: a BEDPE drawn as arcs

Copy number from the published callsets

DRAGEN: integer copy number from short reads

NYGC: a copy ratio, and the genes each segment covers

Wakhan: copy number per parental haplotype

Depth per bin, and B-allele frequency

Keep the BAF track off bigWig summaries

Subclonal copy number

Align the tumor assembly to GRCh38

Walkthroughs

A chr3-chr13 translocation

The same junction three ways

Which calls are drivers

A small deletion in CUZD1

A tandem-repeat call, sized against the normal

Reading copy number

CDKN2A: a homozygous deletion inside a single-copy loss

chr17: loss with LOH, and copy-neutral LOH

KRAS and SMAD4

Synteny and dotplot views of the tumor assembly

Methylation on the tumor reads

Where to go next

Reproduce it end to end

See also

References