UCSC Genome Browser · Tutorial 3

Clinical Examples & Variant Interpretation

From a variant to a call: Recommended Track Sets, regulation, and the evidence

Worked examples in germline & somatic interpretation · genome.ucsc.edu

A thread for today: three cancer variants

A BRCA2 variant: germline, “uncertain significance”. How do experts resolve it? (morning, in the Recommended Track Sets)
The TERT promoter: a non-coding driver. The answer lives in the regulation. (morning, then the epigenetics section)
BRAF V600E: a coding driver in melanoma. Somatic, famous, druggable. (afternoon, the somatic worked example)

Watch them recur We will come back to these variants throughout the session. The goal is that by the end you can interpret a variant, explore its regulatory context, load your own data, and share it as a link.

Interpreting a variant = asking questions (germline)

Databases: ask a question, know which track answers it:

Is it already classified? → ClinVar, ClinGen (germline pathogenicity, ClinVar also carries somatic oncogenicity)
Is it druggable? → CIViC (variant → disease → therapy → evidence)
How often is it seen in tumours? → COSMIC, TCGA Pan-Cancer
Is it just common in healthy people? → gnomAD (germline: common ⇒ likely benign)
Is the gene linked to inherited disease? → GenCC, OMIM
Is the position constrained / in a key domain? → conservation, UniProt, REVEL

Germline first These questions fit a germline variant, and the Recommended Track Sets (next) bundle exactly these tracks. Later we revisit the same questions for somatic cancer variants.

Recommended Track Sets

Hundreds of tracks is overwhelming, start from a curated set

Recommended Track Sets: The problem they solve

The Browser has hundreds of tracks, beginners don’t know which to turn on.
Recommended Track Sets = pre-configured collections for a scenario.
One click turns on a themed set, without changing your locus.
Open via the “Recommended Track Sets” menu item.

Recommended Track Sets dialog — The Recommended Track Sets menu: each link loads a curated, themed set of tracks at your current position.

Seven sets on hg38

Clinical SNVs: disease contribution of coding SNVs
Clinical CNVs: coding structural variants
Non-coding SNVs: functional context of non-coding variants
Determine Exon Relevance: is the variant in a required exon?
Problematic Regions: low-confidence / high-homology regions
ENIGMA BRCA1/BRCA2 VCEP: expert-panel BRCA rules
InSiGHT Lynch Syndrome VCEP: MLH1, MSH2, MSH6, PMS2

coming soon a TP53 Recommended Track Set.

Demo 1: Clinical SNVs (coding) (1/2)

A curated coding-variant workbench:

MANE / RefSeq, UniProt domains
ClinVar, ClinGen, GenCC, GeneReviews, HGMD, LOVD
REVEL + 100-way conservation

A clean pathogenic example here.

Try it, ▶ open the BRCA2 session then open Recommended Track Sets → Clinical SNVs yourself.
Shortcut on dense

We tour the tracks in this set here, then investigate a variant on the next slide.

Clinical SNVs track set at BRCA2 — Clinical SNVs at *BRCA2*: **ClinVar interp** colours each variant pathogenic (red) → VUS (blue) → benign (green), stacked with ClinGen, GenCC, HGMD, LOVD.

Demo 1 · cont: validating a BRCA2 variant (2/2)

Example: BRCA2 c.8167G>C (p.Asp2723His): read the ACMG/AMP codes straight off the Clinical SNVs tracks:

Already classified? → ClinVar Pathogenic (★★★ expert panel) · ClinGen
Same codon / nearby seen before? (PS1 / PM5) → ClinVar
In a key protein domain? (PM1) → UniProt: the DNA-binding domain (OB1 fold) ✓
Computational? (PP3) → REVEL ≈ 0.93 + deep conservation ✓
Loss-of-function? (PVS1) → gene model (missense → N/A)

The point Each line of evidence stacks in one view, here they all agree on pathogenic. You read the ACMG codes off the screen. (The harder, guideline-dependent cases come in the ENIGMA set.)

BRCA2 Asp2723His: RefSeq, UniProt OB1 domain, ClinVar variants — At *p.Asp2723His*: RefSeq/MANE, the **BRCA2 OB1** UniProt domain (PM1), and ClinVar variants stacked.

Demo 2: Non-coding SNVs → epigenetics (1/2)

For variants outside coding exons, the regulatory evidence:

GeneHancer enhancers & enhancer→gene links
Hi-C / Micro-C 3D chromatin contacts
JASPAR TF binding sites · 100-way conservation

Our bridge to epigenetics later in this deck, and the set for our TERT promoter variant.

Non-coding SNVs track set — Non-coding SNVs near the *TERT* promoter: GeneHancer regulatory elements, JASPAR TF sites, and conservation, the context a non-coding variant needs.

Demo 2 · cont: a non-coding variant at TERT (2/2)

TERT promoter hotspot mutations (e.g. C228T · NM_198253.3(TERT):c.-124C>T) sit ~100–150 bp upstream of the start codon, in the core promoter. With the Non-coding set, ask:

In a regulatory element? → GeneHancer / ENCODE cCRE (promoter)
Creates / breaks a TF site? → JASPAR (TF motifs here; the C228T / C250T hotspots create a new ETS / GABPA site that switches TERT back on)
Contacts a distal gene in 3D? → Hi-C / Micro-C (most useful for enhancer variants; less so for this promoter)
Evolutionarily constrained? → conservation

No coding ACMG here Non-coding variants aren’t scored by coding ACMG rules, you weigh the regulatory evidence instead.

Try it, ▶ open the TERT variant session The relevant non-coding tracks are already on.

JASPAR TF sites, cCRE and GeneHancer at the TERT promoter — The *TERT* promoter: ENCODE cCRE, GeneHancer, and dense **JASPAR** TF-binding sites, where the hotspot builds a new ETS site.

Demo 3: expert-panel gene sets

This is the home of our germline BRCA variant of uncertain significance.

ENIGMA BRCA1/BRCA2 VCEP: the exact evidence for ClinGen ENIGMA classification, per exon & variant.
InSiGHT Lynch Syndrome VCEP: same idea for MLH1/MSH2/MSH6/PMS2.

Same variant, different rules, ▶ open it BRCA2 c.830A>G (p.Asn277Ser): standard ACMG reached likely benign (REVEL → BP4); under ENIGMA it reverts to VUS (BayesDel not allowed here; SpliceAI → PP3). The spec can move a call either way; overall it cut VUS, but not for every variant.

Published co-authored Benet-Pagès, Laner, Nassar… Genet Med Open 2025. Session (hg19): /s/abenet/BRCA.ENIGMA.hg19

ENIGMA BRCA1/BRCA2 VCEP track set at a BRCA exon (hg19) — The **ENIGMA BRCA1/BRCA2 VCEP** set at a BRCA1 exon (hg19): the exact evidence the panel rules use, per variant.

Somatic diagnosis

Somatic variants

acquired mutations: SNVs, de novo changes, and cancer drivers

Interpreting a variant = asking questions (somatic)

Databases: ask a question, know which track answers it:

Is it already classified? → ClinVar, ClinGen (germline pathogenicity, ClinVar also carries somatic oncogenicity)
Is it druggable? → CIViC (variant → disease → therapy → evidence)
How often is it seen in tumours? → COSMIC, TCGA Pan-Cancer
Is it just common in healthy people? → gnomAD (germline: common ⇒ likely benign)
Is the gene linked to inherited disease? → GenCC, OMIM
Is the position constrained / in a key domain? → conservation, UniProt, REVEL

Germline vs somatic These questions fit a germline variant. For a somatic driver (BRAF V600E, next) lean on COSMIC & CIViC and ClinVar’s somatic oncogenicity / clinical-impact, and don’t read gnomAD frequency as “benign” (a true somatic variant is just absent). REVEL / conservation flag a damaging residue, not oncogenicity.

Worked example: BRAF V600E

The classic melanoma driver: a somatic variant. BRAF V600E p.Val600Glu NM_004333.6:c.1799T>A chr7:140,753,336

Try it, ▶ open the BRAF V600E session COSMIC: how often in tumours? · CIViC: oncogenic & druggable? · ClinVar: its somatic oncogenicity / clinical-impact, not the germline label.

Somatic ≠ germline gnomAD won’t “verify” it: a true somatic variant is simply absent from healthy-population data (gnomAD filters out inherited variants).

Thread tie-in Our coding driver: the Recommended “Clinical SNVs” set assembles these in one click.

BRAF V600E with ClinVar, COSMIC and CIViC (hg38 session) — The *BRAF V600E* session: ClinVar, COSMIC and CIViC stacked at chr7:140,753,336.

Expression

where, and in which cell type, is a gene switched on?

Three expression datasets on hg38

“Where is my gene expressed, and in which cell type?” Two axes separate these tracks: bulk tissue vs single cell, and one uniform study vs many pooled together.

GTEx Gene V8

Bulk tissue reference.

54 tissues, 948 donors, bulk RNA-seq. On by default.
Each bar is a whole tissue, so every cell type is averaged together.
Best for: which organ is the gene expressed in?

Tabula Sapiens

One uniform single-cell atlas.

~480k cells across ~24 organs, one consortium, consistent processing.
Bars split by tissue and by cell type, so signal resolves to a cell type.
Best for: which cell type, answered cleanly within one atlas.

Merged Single-Cell

Many single-cell studies at once.

Pools many published atlases (incl. Tabula Sapiens) into one track.
Widest coverage, but heterogeneous: compare within a dataset, not across.
Best for: does it hold across the single-cell literature?

GTEx gives you the organ, the single-cell tracks refine it to the cell type, and the merged track checks whether it holds across many studies.

Expression: what tissue is it expressed in?

Expression tracks at KLK3 on hg38: GTEx, Tabula Sapiens, and single-cell — *KLK3* on hg38 with GTEx, Tabula Sapiens, and single-cell tracks: the signal spikes in prostate luminal epithelium and is near-silent elsewhere.

KLK3 encodes PSA (prostate-specific antigen), the protein behind the prostate-cancer blood test. Expression is restricted to prostate luminal epithelium, so the contrast is clear.

Our thread genes (BRAF, TERT, BRCA2) are broadly expressed, so we pick a textbook tissue-specific gene to make the contrast obvious.

Try it Look up KLK3; read its GTEx bars (prostate towers over the rest), then Tabula Sapiens for the cell type. Load the session.

Regulation & epigenetics

enhancers · histone marks · open chromatin · methylation

Picking up from “Non-coding SNVs”

That set pointed us at GeneHancer, Hi-C/Micro-C, JASPAR and conservation.
They all live in the Regulation group, largely from ENCODE.

Heads-up Two “Regulation” super-tracks exist: ENCODE3 & ENCODE4. Use ENCODE4.

Try it, ▶ a real 3D loop at MYC HFFc6 Micro-C: a stripe off the MYC promoter reaches a loop dot in the 8q24 enhancer desert (chr8:128.31–128.33 Mb).

Regulation track group controls — The **Regulation** group: ENCODE cCREs, DNA Methylation, GeneHancer, Hi-C and Micro-C, JASPAR, VISTA Enhancers and more, all in one place.

Re-anchor to the Non-coding SNVs set, then open the Regulation group fully. Warn early about the two Regulation super-tracks so nobody turns on the older ENCODE3 one by mistake. MYC / 8q24 3D demo (session s/Lou/MYCcontact): a clear, cancer-relevant way to introduce Hi-C / Micro-C. Display HFFc6 Micro-C with KR normalization at ~10 kb and the colour max turned down (square or triangle). The bright vertical stripe off the MYC promoter (highlighted blue) is an architectural stripe from loop extrusion: cohesin reels the fibre through while MYC's anchor is held, so MYC samples the whole downstream domain. It is a population average over millions of cells, not all-at-once contact; in any single cell MYC touches one spot. The stripe ends in a loop dot at chr8:128,310,000-128,330,000 (O/E ~10), an intergenic CTCF / boundary anchor that frames the regulatory domain (TAD). Teaching beat: Micro-C shows what CAN reach MYC in 3D (the domain); the distal-enhancer cCREs / H3K27ac show which of those are actually regulatory. MYC has a large, tissue-specific enhancer repertoire across the 8q24 desert (dozens across cell types), only a subset active in any one cell. If you widen the view, MYC's strongest contacts of all sit further out, around chr8:128.65-128.93 Mb.

Enhancers & promoters: cCREs

ENCODE4 cCREs: candidate cis-regulatory elements, on by default.
Colour = type: red = promoter-like, orange = enhancer-like, + CTCF / accessible-only.
Mouse-over gives DNase / H3K4me3 / H3K27ac / CTCF scores; filter by class.

One track answers “enhancer or promoter, and is it active?”.

Try it We put this to work on the TERT promoter in a few slides.

ENCODE4 cCREs at the GAPDH promoter — cCREs around the *GAPDH* promoter: a **red** promoter-like element at the TSS, flanked by **orange** enhancer-like elements.

Histone marks & open chromatin

ENCODE4 DNase, ATAC, H3K4me3 and H3K27ac at the GAPDH promoter — Upstream of *GAPDH*: ENCODE4 **DNase** & **ATAC** (open chromatin) with **H3K4me3** & **H3K27ac** peaks: the signature of an active promoter.

Histone marks (ChIP-seq): H3K27Ac, H3K4Me1, H3K4Me3, layered by cell line.
Promoter open or closed? → DNase & ATAC.

Try it Open the GAPDH promoter session: DNase + ATAC with H3K4me3 & H3K27ac.

Worked locus: the TERT promoter (1/2)

The classic non-coding driver (melanoma, glioma, bladder), the third variant in our thread.
At the TSS: a red promoter cCRE under H3K27ac: a textbook open, active promoter.

Try it, ▶ open TERT with cCREs + H3K27Ac + DNase + GeneHancer Then try MYC 8q24 for an enhancer landscape.

cCREs at the TERT promoter — The *TERT* promoter: the **red** ENCODE4 cCRE = promoter-like; orange = enhancer-like. Layered H3K27Ac and conservation sit alongside.

TERT: the data say “active promoter” (2/2)

Active-promoter signals at TERT — At the *TERT* TSS: red promoter cCRE + H3K27Ac + a DNase peak + GeneHancer: every track points to an active promoter.

cCRE coloured red = promoter-like element
H3K27Ac peak = active regulatory region
DNase hypersensitivity = open chromatin
GeneHancer marks the TERT TSS / regulatory element

Conclusion Independent assays converge → a real, active promoter, exactly where a non-coding driver mutation bites.

A variant-interpretation toolkit

AI predictors and population frequencies

AlphaMissense: AI missense pathogenicity

Google DeepMind's deep-learning score for every possible missense substitution (Cheng et al., Science 2023).
One value per change, 0 to 1: likely benign → ambiguous → likely pathogenic, drawn at base resolution.
The AI counterpart to REVEL: another computational line of evidence (ACMG PP3) for a coding variant.

Try it Open the AlphaMissense track at BRAF V600 or BRCA2; read the score for our variants.

Why it fits We leaned on REVEL for PP3 earlier. AlphaMissense is the same idea, trained by an AI model, and it covers the whole protein so you can scan a gene for predicted hotspots.

SpliceAI: predicting splice disruption

Illumina's deep-learning predictor of whether a variant creates or breaks a splice site (Jaganathan et al., Cell 2019).
Delta scores (0 to 1) for acceptor / donor gain & loss, with the predicted base position.
Lives under the Splicing Impact super-track; SNVs and indels, raw and masked.

Try it Open Splicing Impact and inspect a splice-region variant.

Closes a loop The ENIGMA demo (Demo 3) cited SpliceAI → PP3 as the evidence that moved a BRCA2 call. This is that track: see the score that drove the reclassification.

Variant frequencies: how common, everywhere

SNV Frequencies: allele frequencies gathered from population-scale projects worldwide, ~1.7 million genomes / exomes / arrays.
One place to compare how common a variant is across populations, ancestries and cohorts, including national projects not in gnomAD.
Combined tracks aggregate the data, plus one subtrack per project (TOPMed, gnomAD, 1000 Genomes, and more).
note Collected as-is, not re-harmonized: pipelines and assays differ between projects.

Try it Open the SNV Frequencies track (on dev).

What you can now do

Turn a variant into a set of answerable questions, germline and somatic.
Load a Recommended Track Set and read ACMG-style evidence off the tracks.
Interpret a non-coding variant from its regulatory context.
Bring in AI predictors (AlphaMissense, SpliceAI) as extra lines of evidence.

Where to get help

Help → Interactive Tutorials (in the menu bar).
Documentation & tutorials: genome.ucsc.edu/docs
Email the team: genome@soe.ucsc.edu
All contact options: genome.ucsc.edu/contacts.html

Thank you!

Questions? · genome@soe.ucsc.edu

UCSC Genome Browser · genome.ucsc.edu