A short orientation for the people trying this before anyone else. Everything below is a number the platform computed and stored, with the date it was measured — not an estimate, and not a claim about your assay. Sign in at blindspotgenetics.com/login.html: enter your email address and we email you a six-digit code; no password needed. A shared demo login is a username, not an address — type it exactly as given.
Short-read sequencing cannot see every base of every gene. Segmental duplications, low-complexity tracts, paralogues that differ by a handful of bases, and stretches where the reference genome itself is wrong or absent all produce regions where a variant can be present and simply not called. Those regions are known, documented and measurable — but they are usually invisible at the point where someone is choosing a test or reading a negative result.
Blindspot maps them gene by gene, on both of the human reference assemblies in current use, and says how much of each gene's coding sequence sits inside one. It carries 19,250 genes: 5,694 at clinical tier, which are assessed in full, and 13,556 at research tier. It is a reference tool. It does not interpret variants, it does not see your data, and it is not diagnostic.
All figures on this page measured 14 October 2026
Seven results, in the order they matter. Each one is reproducible from the database the site runs on, and most of them can be seen on a gene card in a few seconds.
19,150 of 19,338 genes — 99.0% — have at least one base inside GIAB's
alldifficultregions track on GRCh38. A flag that fires on 99% of genes tells
you nothing about the gene in front of you.
So the platform does not report whether a gene touches a difficult region. It reports how much of the coding sequence sits inside one, which tracks are responsible, and whether the answer changes between references. That is the difference between a warning and a measurement.
Not merely difficult: every base of the exon lies in a GIAB segmental duplication or low-mappability region, where a read may align to another copy of the sequence instead of this one. On the gene card's exon map they are red where the sequence is low mappability, which short reads cannot place, and magenta where it is segmental duplication alone, where reads usually still place but a variant matching the other copy can be misassigned. These are the genes where a negative result for that exon carries the least information. They are 8.6% of the clinical panel.
For 181 of them the gene as a whole still rates Reliable or Caution — CFTR and TTN among them. The class describes the whole coding sequence, and one exon is a small share of it; the exon map on each card shows where the gap is.
Every gene is placed on a five-step ladder. The two ends are unsurprising; the middle three are the reason the ladder exists, because they fail for three different reasons and need three different responses.
| Class | Clinical | Research | All genes |
|---|---|---|---|
| 1 Reliablecallable end to end | 3,452 | 7,293 | 10,745 |
| 2 Cautiona region worth knowing about | 1,802 | 4,522 | 6,324 |
| 3 Reference-sensitivethe answer depends on which reference | 75 | 320 | 395 |
| 4 Technology-sensitivethe answer depends on the method | 303 | 1,033 | 1,336 |
| 5 Refractoryno short-read method sees it | 62 | 388 | 450 |
440 clinical genes — one in thirteen — are class 3, 4 or 5. That is the population this ladder exists for. It is not the whole population with a blind spot, and reading it that way is the mistake the next finding is here to stop.
The class ladder asks can the sequence be read. It does not ask what can the assay not see at all, and those are different questions. A gene can be perfectly callable base by base and still hide a whole-exon deletion.
2,193 clinical genes — 38.5% of the panel — are class 1 or 2 and still carry a documented pathogenic deletion or duplication. That is 5.0 times the class 3/4/5 population, and it lands almost entirely on the genes the ladder calls readable. 1,687 of them have an event that fits entirely inside the gene, and for 1,533 of those the median event is under 50 kb — exon scale.
Why small-variant calling shows nothing: a heterozygous deletion leaves the remaining copy perfectly readable. Every base returns a correct call. What reveals the loss is the read count, not the read content, and small-variant calling does not look at the count.
This is not a claim that sequencing cannot find these events. Depth-based copy-number calling from the same reads finds the larger ones, and whether a given laboratory runs it — with which caller, at which resolution — is a step inside that laboratory's own validated pipeline. This platform has never measured that and does not name callers. What it measures is where the event sits relative to the gene, because that is what decides which assay resolves it.
CHD1L, NECAP1 and OPRL1 each measure 0.0% confidently callable on GRCh38 and 100.0% on T2T-CHM13. Nothing about the sequencing changed. The same reads, the same chemistry, the same pipeline — a different reference, and a gene goes from unreadable to complete.
Across the clinical panel, 121 genes score better on CHM13 and 47 score better on GRCh38. The direction is not uniform, which is exactly why the platform carries both rather than picking one.
CFHR1 and CFHR3 are 0.0% on GRCh38 and have no native CHM13 gene model at all. Their criteria codes name the causes together — no benchmark-validatable coding sequence on GRCh38, segmental duplication, low mappability, and a benchmark that withdrew its earlier confidence — with no CHM13 model to fall back on. Class 5 is not a warning to read more carefully. It is a statement that this method does not answer the question, and the card says which codes led to it.
T2T-CHM13 numbers can be computed against either a native gene model — one annotated directly on that assembly — or a Liftoff projection, a GRCh38 model mapped across. A sweep of all 20,064 protein-coding placements found 721 (3.6%), over 528 genes, where the projection has no native model to corroborate it.
The clearest case: KRT81 carried a CHM13 figure of 100.0. Native CHM13 annotates no KRT81 there at all — the projection lands on the interval annotated as KRT86, a named keratin paralogue. The stored figure described a different gene. Three more (OR1B1, PLA2G10, ZAN) were withdrawn with it.
Twelve more were withdrawn on 2 October. Their projections land on the right gene, but the transcript every GRCh38 figure is measured on is not annotated on CHM13, so the number described a projection, not a native model. One of them, TBCD, had been Reference-sensitive on that number alone and is now class 2. Every CHM13 figure on the site is now measured on a native model.
The genes themselves are still carried, and their cards now say the placement is unverified rather than showing a number. A withdrawn figure is a finding.
Of the 19,250 symbols carried here, 1,484 (7.7%) have an adjacent-transposition variant that is another real symbol on this platform. Restricted to the case where both are clinical: 165 genes. AMT and ATM. NF1 and FN1. PKD1 and PDK1.
Type one when you meant the other and you get a complete, accurate, entirely confident card — about a gene you did not ask for. Cards now disclose the neighbor, and a symbol that is not a gene at all offers the near miss instead of a dead end. Worth testing: search RMB20, then look at the ATM card.
Enough to read a card. The full list of classes, criteria codes and difficulty tracks lives on the definitions page.
The fraction of a gene's coding sequence that sits inside the GIAB high-confidence benchmark region for a given reference — the part where a call can be trusted and, just as importantly, where a no-call means something.
Shown per reference, GRCh38 and CHM13
A GIAB stratification track: segmental duplications, tandem repeats, low mappability, GC extremes and others. A gene can overlap several. The tracks are the reason behind a class, which is why cards name them rather than summing them.
Source: GIAB stratifications, see Data releases
1 Reliable · callable end to end. 2 Caution · a region worth knowing about. 3 Reference-sensitive · the answer changes with the reference. 4 Technology-sensitive · it changes with the method. 5 Refractory · no short-read method resolves it.
3, 4 and 5 need different responses
The two reference assemblies. GRCh38 is what almost every clinical pipeline aligns to and still contains gaps and errors. T2T-CHM13 is gapless, and closes some of them while being unsupported by most clinical tooling. Both are carried; neither is treated as the truth.
121 clinical genes score better on CHM13, 47 on GRCh38
A native CHM13 figure was measured against a gene model annotated on CHM13 itself. A Liftoff figure was measured against a GRCh38 model mapped across. A projection can land on a paralogue and produce a confident number about the wrong sequence, which is what happened to four genes here; twelve more were measured on a projection of a transcript CHM13 does not annotate. All sixteen figures are withdrawn.
Cards say which one a figure came from
Carried beside the class, never folded into it, because it answers a different question: not can the sequence be read but what can the assay not see at all. A gene can be class 1 and still carry a red copy-number state.
Six states, from "not assessed" to a gene-specific del/dup
A pathogenic copy-number event that overlaps the gene and spans at most three genes. Counted by position and focality, with no size term — because size cannot decide which gene an event is about. The card shows how many lie inside the gene, cover one end, or cover the whole of it.
Source: ClinVar copy-number records
A coding exon lying entirely in a GIAB segmental duplication or low-mappability region, where a short read may align to another copy — the strongest form of a blind spot the card draws. 490 clinical genes have at least one. The exon map separates the two: red for low mappability, where short reads cannot place the exon, and magenta for segmental duplication alone, where they usually can.
Per-exon, not per-gene
Clinical-tier genes (5,694) are assessed in full against the benchmark. Research-tier genes (13,556) are carried and classified, but some measurements are absent rather than zero — and the card says not measured, which is not the same claim as zero.
The distinction is visible on every card
Not a term, a rule. Where the platform has no measurement it says so, rather than rendering a blank, a zero, or a confident sentence. Most of the defects found in building this were versions of the same mistake.
If you see silence where you expect a caveat, report it