Is a measurable share of published variant-caller accuracy an artefact of the regions GIAB v4.2.1 did not cover — re-run on GIAB HG002 v5.0q, is the F1 of published callers lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum?
Non-exhaustive sources that may help: - GIAB HG002 benchmark v5.0q (T2T-HG002 v1.1 based; 99.9% of the diploid genome vs 85.2% for v4.2.1; +918 Mb newly benchmarked incl. chrX/Y) — https://www.nist.gov/programs-projects/genome-bottle - Hansen N. et al., 'A complete diploid human genome benchmark for personalized genomics', Cell 189:4857-4875.e31 (2026) — https://doi.org/10.1016/j.cell.2026.06.016 - NIST reference materials RM 8391 / 8392 / 8393 / 8398 (HG002, trio, HG005, HG001) — https://www.nist.gov/programs-projects/genome-bottle - GIAB/NIST stratification BEDs v3.6 incl. SegmentalDuplications, LowMappability, homopolymers (GRCh37/38, CHM13, HG002-Q100) — https://github.com/usnistgov/giab-stratifications - Dwarshuis N. et al., stratifications description, Nat Commun 15 (2024) — https://doi.org/10.1038/s41467-024-53260-y - GIAB CMRG benchmark v1.00 -- 273 of 395 challenging medically relevant genes (SMN1 etc.); Wagner J. et al., Nat Biotechnol (2022) — https://doi.org/10.1038/s41587-021-01158-1 - nf-core/sarek v3.10.0 'Aktse' (12 Aug 2026), MIT — https://nf-co.re/sarek - RTG Tools / vcfeval v3.13 (BSD-2) -- actively maintained; hap.py v0.3.15 (2021) -- dormant but still the GA4GH reference implementation — https://github.com/RealTimeGenomics/rtg-tools - Olson N.D. et al., 'Variant calling and benchmarking in an era of complete human genome sequences', Nat Rev Genet 24:464-483 (2023) — https://doi.org/10.1038/s41576-023-00590-0 - Qin Q. & Li H., SV calling in low-complexity regions vs HG002-Q100 v1.1; 77.3-91.3% of erroneous SV calls fall in LCRs, GigaScience (2025) — https://doi.org/10.1093/gigascience/giaf154
The calibrated literature ladder found broad HG002 benchmarking literature but zero matches for the exact old-versus-new regional comparison, including the query “("GIAB v4.2.1" AND "HG002 v5.0q") AND (old OR previous) AND (new OR newly) AND regions.” None of the listed hits has a title indicating that published callers’ F1 was compared between v4.2.1-covered and newly benchmarked regions per stratum. The digest also confirms that this partitioned analysis is methodologically appropriate, while aggregate error totals alone cannot answer it.
solver budget spent
The solution contains substantive factual and methodological errors. First, it says the v5 benchmark manuscript is “forthcoming,” but Hansen et al. was published in Cell on August 6, 2026. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913?utm_source=openai)) Second, the larger FP+FN totals under full v5.0q do not establish that any measurable share of prior accuracy was an artefact of omitted regions: v5.0q changes both the evaluated regions and the truth calls, and raw error totals also increase with the number of assessed bases and variants. Without partitioning v5.0q results into old and newly benchmarked regions, the extra errors cannot be attributed specifically to newly covered regions. Third, the stated formula F1=2TP/(2TP+FP+FN) is not generally valid for hap.py output because hap.py separately reports TRUTH.TP for recall and QUERY.TP for precision; F1 should be taken as the harmonic mean of METRIC.Recall and METRIC.Precision. ([github.com](https://github.com/Illumina/hap.py/blob/master/doc/happy.md?utm_source=openai)) Therefore the aggregate “yes” is unsupported, even though the proposed paired old/new analysis is broadly the right way to answer the question.
You are one role in an automated verified-reasoning pipeline. Your reply is parsed by a program: return exactly what is asked, nothing else. You have no shell or filesystem. Use the web_search tool to verify facts, citations, and definitions rather than relying on recall. Use the code interpreter (Python; sympy/numpy/scipy available) to recompute rather than trusting arithmetic. Solve the given problem. Show your reasoning. Use web search for anything you're even remotely unsure about.
Is a measurable share of published variant-caller accuracy an artefact of the regions GIAB v4.2.1 did not cover — re-run on GIAB HG002 v5.0q, is the F1 of published callers lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum? Non-exhaustive sources that may help: - GIAB HG002 benchmark v5.0q (T2T-HG002 v1.1 based; 99.9% of the diploid genome vs 85.2% for v4.2.1; +918 Mb newly benchmarked incl. chrX/Y) — https://www.nist.gov/programs-projects/genome-bottle - Hansen N. et al., 'A complete diploid human genome benchmark for personalized genomics', Cell 189:4857-4875.e31 (2026) — https://doi.org/10.1016/j.cell.2026.06.016 - NIST reference materials RM 8391 / 8392 / 8393 / 8398 (HG002, trio, HG005, HG001) — https://www.nist.gov/programs-projects/genome-bottle - GIAB/NIST stratification BEDs v3.6 incl. SegmentalDuplications, LowMappability, homopolymers (GRCh37/38, CHM13, HG002-Q100) — https://github.com/usnistgov/giab-stratifications - Dwarshuis N. et al., stratifications description, Nat Commun 15 (2024) — https://doi.org/10.1038/s41467-024-53260-y - GIAB CMRG benchmark v1.00 -- 273 of 395 challenging medically relevant genes (SMN1 etc.); Wagner J. et al., Nat Biotechnol (2022) — https://doi.org/10.1038/s41587-021-01158-1 - nf-core/sarek v3.10.0 'Aktse' (12 Aug 2026), MIT — https://nf-co.re/sarek - RTG Tools / vcfeval v3.13 (BSD-2) -- actively maintained; hap.py v0.3.15 (2021) -- dormant but still the GA4GH reference implementation — https://github.com/RealTimeGenomics/rtg-tools - Olson N.D. et al., 'Variant calling and benchmarking in an era of complete human genome sequences', Nat Rev Genet 24:464-483 (2023) — https://doi.org/10.1038/s41576-023-00590-0 - Qin Q. & Li H., SV calling in low-complexity regions vs HG002-Q100 v1.1; 77.3-91.3% of erroneous SV calls fall in LCRs, GigaScience (2025) — https://doi.org/10.1093/gigascience/giaf154
{
"summary": [
"**Figuring out analysis needs**\n\nI need to address the user's request for an empirical re-run. This involves possibly formulating an analysis or obtaining published datasets for benchmarking. I should identify supplementary data from a future paper, maybe from Cell 2026 or its v5 release. It seems I need to do some web searching. The user is asking about data per stratum, so I’ll likely want to reference figures and tables from Hansen's paper as well."
]
}{
"action": {
"type": "search",
"queries": [
"\"A complete diploid human genome benchmark for personalized genomics\" v5.0q caller F1 newly benchmarked regions",
"GIAB HG002 v5.0q newly benchmarked regions F1 DeepVariant Clair3 per stratum",
"site:github.com GIAB v5.0q HG002 benchmark comparison callers stratifications",
"site:nist.gov HG002 v5.0q benchmark files"
],
"query": "\"A complete diploid human genome benchmark for personalized genomics\" v5.0q caller F1 newly benchmarked regions"
}
}{
"action": {
"type": "open_page",
"url": "https://www.sciencedirect.com/science/article/pii/S0092867426007038"
}
}{
"action": {
"type": "search",
"queries": [
"\"S0092867426007038\" supplementary data",
"\"Table S17\" \"HG002\" v5.0q",
"\"v5.0q\" \"DeepVariant\" HG002 F1",
"\"HG002_GRCh38_v5.0q\" caller benchmark"
],
"query": "\"S0092867426007038\" supplementary data"
}
}{
"summary": [
"**Searching for data sources**\n\nI need to find and possibly download call sets from the paper trace archive, specifically Table S13. I might also check if there are any benchmark published callers shown in Figure 2. It sounds like a good idea to search for snippets with specific terms related to these sources. This should help me gather more relevant information effectively and ensure I have what I need to proceed!"
]
}{
"action": {
"type": "search",
"queries": [
"site:sciencedirect.com/science/article/pii/S0092867426007038 \"variant callers\" \"Figure 2\"",
"\"HG002v1.1\" \"F1\" DeepVariant",
"\"newly benchmarked\" \"v4.2.1\" HG002",
"\"variant calling\" \"99.4%\" HG002 DeepVariant F1"
],
"query": "site:sciencedirect.com/science/article/pii/S0092867426007038 \"variant callers\" \"Figure 2\""
}
}{
"action": {
"type": "open_page",
"url": "https://www.sciencedirect.com/science/article/pii/S0092867426007038"
}
}{
"action": {
"type": "search",
"queries": [
"\"S0092867426007038\" \"mmc\"",
"\"S0092867426007038\" filetype:xlsx",
"\"A complete diploid human genome benchmark\" \"Table S13\"",
"\"A complete diploid human genome benchmark\" \"Figure 2\" variant call sets"
],
"query": "\"S0092867426007038\" \"mmc\""
}
}{
"summary": [
"**Investigating PMC results**\n\nI'm noticing that there might be an issue with a paper due to mismatched results. I'll need to query NCBI PMC to gather more information. The PMID might have figures and possibly supplemental data I can look at. Checking the full page snippets on PubMed could also provide helpful figure legends. I should explore the open search results to see what lines I can follow for clarity. This could help me get to the bottom of things!"
]
}{
"action": {
"type": "search",
"queries": [
"site:pubmed.ncbi.nlm.nih.gov/42561913 \"Figure 5\"",
"site:pubmed.ncbi.nlm.nih.gov/42561913 \"Figure 3\"",
"site:pubmed.ncbi.nlm.nih.gov/42561913 \"variant-constructed\""
],
"query": "site:pubmed.ncbi.nlm.nih.gov/42561913 \"Figure 5\""
}
}{
"summary": [
"**Considering a mini research project**\n\nThe user might be anticipating a mini computational research project. I should be honest that I can't rerun anything unless I have access to the data. However, I can provide an answer based on existing outputs if I can obtain benchmark stats from call sets. There's mention of the GIAB v5 benchmark and possibly some example comparisons and outputs from NIST. I could search for FTP indexing to support this."
]
}{
"action": {
"type": "search",
"queries": [
"site:ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis \"v5.0q\"",
"site:ftp-trace.ncbi.nlm.nih.gov \"HG002_GRCh38_v5.0q\"",
"site:github.com/usnistgov \"v5.0q\" hap.py",
"site:github.com/usnistgov \"newly benchmarked\" HG002"
],
"query": "site:ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis \"v5.0q\""
}
}{
"summary": [
"**Evaluating benchmark integration**\n\nI’m considering using the GitHub API to access GIAB benchmark files and calls from DeepVariant. However, the actual benchmarking might need shell access. I could download VCF files and utilize Python libraries. That sounds big but potentially doable! Still, no user-visible artifacts are needed, and private Python access isn't available. I need a way to determine if answers are qualitatively “yes, universally lower.” I'll search for reports comparing v4 vs. v5 on the same callsets."
]
}{
"action": {
"type": "open_page",
"url": "https://www.sentieon.com/?page_id=931"
}
}{
"action": {
"type": "open_page",
"url": "https://www.sentieon.com/?page_id=931"
}
}{
"action": {
"type": "search",
"queries": [
"\"NIST v5.0q\" \"LowMappability\" F1 HG002",
"\"v5.0q\" \"SegmentalDuplications\" F1",
"\"v5.0q\" \"homopolymer\" \"F1\" HG002",
"\"Q100\" \"NotInAllDifficult\" HG002 F1"
],
"query": "\"NIST v5.0q\" \"LowMappability\" F1 HG002"
}
}{
"summary": [
"**Extracting figure values**\n\nI'm thinking about extracting values from Figure 2B in PanVariants. I guess there might not be a distinction between old and new values. It seems like I need to click directly to access the figure. I want to make sure I'm getting the right information without missing anything. Let's see what happens when I try to get those values directly — that might be the safest way to ensure I'm accurate!"
]
}{
"action": {
"type": "open_page",
"url": "https://www.biorxiv.org/content/10.64898/2026.04.22.720142v1.full"
}
}{
"action": {
"type": "search",
"queries": [
"HG002 v5.0q truth variants count SNV indel",
"\"HG002_GRCh38_v5.0q\" variants",
"\"v5.0q\" \"truth\" SNV indel count",
"\"NIST_HG002_DraftBenchmark_defrabbV0.019\" variant count"
],
"query": "HG002 v5.0q truth variants count SNV indel"
}
}{
"summary": [
"**Clarifying benchmarks**\n\nI’m thinking I should clarify that the v5.0q benchmark is focused on variants with respect to a reference and covers 99.9% of the diploid genome, specifically excluding rDNA. But I wonder if the small variant benchmark might not reach 99.9% — I'll need to check on that for accuracy. It seems the user may be confusing the v5.0q and T2T genome benchmarks. I’ll search for the details officially from NIST."
]
}{
"action": {
"type": "open_page",
"url": "https://www.nist.gov/programs-projects/genome-bottle"
}
}{
"action": {
"type": "open_page",
"url": "https://www.nist.gov/programs-projects/genome-bottle"
}
}{
"action": {
"type": "open_page",
"url": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/v5.0q/"
}
}{
"action": {
"type": "search",
"queries": [
"https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/v5.0q/ README",
"\"HG002_NA24385_son/v5.0q/README\"",
"\"v5.0q\" \"README\" GIAB"
],
"query": "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/v5.0q/ README"
}
}{
"summary": [
"**Evaluating stratification issues**\n\nI'm considering a potential issue with RTG vcfeval regarding stratification output after benchmarks, especially if variants cross boundaries. While hap.py supports stratification, it's complicated with old and new subsets. The data strongly indicates that the claim may not be fully supported. If someone asks for a re-run, I'll need to show my reasoning and possibly provide a reproducible plan. I think it’s essential to quantify raw errors to demonstrate the effects clearly.",
"**Considering visual ratios**\n\nI’m thinking it’s probably best to avoid exact approximations in my analysis and instead focus on visual ratios. I want to ensure that the information I'm presenting is easily interpretable without getting too caught up in precise numbers that might confuse things. It seems like emphasizing visual relationships can make it clearer and more effective for the user to understand the data at a glance. Simplifying it this way could enhance comprehension overall!"
]
}{
"code": "old = {\n\"DNAseq\":[5103,9125,28204,26815],\n\"DNAscope\":[1022,2629,5301,18197],\n\"DNAscope Pangenome\":[454,963,1338,2850],\n\"DRAGEN 4.5\":[1229,1026,1998,2198]\n}\nnew={\n\"DNAseq\":[26291,105423,41858,62446],\n\"DNAscope\":[15841,48046,15952,42465],\n\"DNAscope Pangenome\":[9299,30005,3698,15163],\n\"DRAGEN 4.5\":[19661,36945,5764,16490]\n}\nfor k in old:\n print(k,sum(old[k]),sum(new[k]),sum(new[k])/sum(old[k]))\n",
"outputs": []
}## Conclusion
**The available evidence says the old benchmark materially overstated genome-wide performance, but a defensible “lower F1 in new versus old regions, per GIAB stratum” result has not yet been published or demonstrated by the cited sources.** A fresh, paired `hap.py`/`vcfeval` analysis is required.
GIAB now treats HG002 v5.0q as replacing v4.2.1, but NIST still describes the accompanying v5 benchmark manuscript as forthcoming. Also, the often-quoted **99.9% versus 85.2%** comparison concerns high-confidence coverage of the *diploid genome benchmark* after excluding rDNA—not directly the fraction of GRCh38’s hap.py small-variant benchmark BED. ([nist.gov](https://www.nist.gov/programs-projects/genome-bottle))
### What can already be measured
Sentieon published paired error counts for several frozen HG002 Illumina callsets against v4.2.1 and v5.0q:
| Caller | FP+FN, v4.2.1 | FP+FN, v5.0q | Increase |
|---|---:|---:|---:|
| DNAseq | 69,247 | 236,018 | 3.41× |
| DNAscope | 27,149 | 122,304 | 4.50× |
| DNAscope Pangenome | 5,605 | 58,165 | 10.38× |
| DRAGEN 4.5 | 6,451 | 78,860 | 12.22× |
These sums are recomputed from the four reported categories—SNV FP/FN and indel FP/FN. ([sentieon.com](https://www.sentieon.com/wp-content/uploads/2026/06/Figure1-1.jpg))
Thus, **a measurable share of the very small published error totals was conditional on what v4.2.1 omitted**. The effect is not marginal: v5.0q exposes tens to hundreds of thousands of additional errors in these examples. Hansen et al. independently show why this occurs: the new benchmark adds about 701.4 Mb of autosomal sequence plus 216.8 Mb of sex-chromosome sequence and includes duplicated, structurally polymorphic and repetitive regions systematically underrepresented before. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913/?utm_source=openai))
But those totals do **not** answer the requested per-stratum F1 question:
1. FP+FN does not determine F1 without TP counts.
2. v5.0q changes truth calls as well as benchmark coverage.
3. It adds X/Y, confounding “new region” with chromosome composition.
4. Published v5 stratified analyses generally compare callers across strata, not the same stratum split into `v4-covered` and `new-in-v5` portions. ([biorxiv.org](https://www.biorxiv.org/content/10.64898/2026.04.22.720142v1.full?utm_source=openai))
## Correct paired re-analysis
For each reference and caller, define both partitions using the **v5.0q truth and benchmark BED**:
\[
R_{\rm old}=B_{v5}\cap B_{v4.2.1}
\]
\[
R_{\rm new}=B_{v5}\setminus B_{v4.2.1}.
\]
For every GIAB v3.6 stratum \(S_j\):
\[
R_{{\rm old},j}=R_{\rm old}\cap S_j,\qquad
R_{{\rm new},j}=R_{\rm new}\cap S_j.
\]
Benchmark the identical frozen caller VCF twice against v5.0q:
```bash
hap.py HG002_GRCh38_v5.0q_truth.vcf.gz CALLER.vcf.gz \
-f old_in_stratum.bed \
-r GRCh38_GIAB.fa \
--engine vcfeval --gender male \
-o caller.old.stratum
hap.py HG002_GRCh38_v5.0q_truth.vcf.gz CALLER.vcf.gz \
-f new_in_stratum.bed \
-r GRCh38_GIAB.fa \
--engine vcfeval --gender male \
-o caller.new.stratum
```
Then calculate separately for SNVs and indels:
\[
F1=\frac{2TP}{2TP+FP+FN},\qquad
\Delta F1_j=F1_{{\rm new},j}-F1_{{\rm old},j}.
\]
The analysis should separate:
- newly covered autosomal regions;
- chromosome X non-PAR;
- chromosome Y non-PAR;
- PAR;
- all sex chromosomes combined.
Otherwise, an apparent new-region penalty could simply reflect the total absence of X/Y from v4.2.1.
Block-bootstrap genomic intervals—rather than individual variants—to obtain confidence intervals, since errors cluster in repeats. Strata with too few truth variants should be reported as underpowered rather than assigned unstable F1 values.
## Expected—but not yet measured here—stratum pattern
The evidence supports the following directional hypotheses:
| Stratum | Expected new-region F1 |
|---|---|
| Segmental duplications | Substantially lower |
| Low mappability | Substantially lower |
| Satellites/centromeric repeats | Substantially lower |
| Long tandem repeats | Lower, especially indels |
| Long homopolymers | Lower, strongly technology-dependent |
| `NotInAllDifficult` | Small or possibly no material decrease |
| Low/high GC | Caller- and library-dependent |
| X/Y and PAR | Must be reported separately |
GIAB’s stratification work already finds performance highest outside difficult regions and diagnoses very poor results in improperly handled PAR regions; Hansen et al. also report reduced consensus quality when newly accessible satellite-rich sequence is included. ([nature.com](https://www.nature.com/articles/s41467-024-53260-y?utm_source=openai))
**Bottom line:** **yes at the aggregate error-count level; probably yes for most repeat/mappability strata; but “yes per stratum” remains unverified until v5.0q is benchmarked with complementary old/new masks using the same v5 truth.** Reporting full-v5 versus v4.2.1 F1 alone is not sufficient to establish that result.You are one role in an automated verified-reasoning pipeline. Your reply is parsed by a program: return exactly what is asked, nothing else. You have no shell or filesystem. Use the web_search tool to verify facts, citations, and definitions rather than relying on recall.
You formalize solutions into proofs. The solutions you receive were
generated by an LLM and may contain hallucinations, wrong facts, or
flawed reasoning: treat every claim as potentially wrong, and do not
assume the solver's reasoning is valid unless you can justify it.
On every call, you must choose one of two actions:
- action="proof": Produce a formal proof for the solution (see
proof format below). This is the default — start by trying to
formalize.
- action="reject": If while formalizing you find any substantive
issues with the solution (incorrect facts, flawed reasoning,
missing cases, etc.), reject the solution and set reject_reason
to explain what's wrong; the solver will get this and retry.
A proof is a sequence of states. Each state is a list of strings.
Index 0 is the expression being solved for. It starts as a goal
variable (like "ANSWER") and ends when resolved.
Each step has: state (full list of strings after this step),
justification_type ("citation", "problem_given", or "computation"),
and justification (why).
One transformation per step. Every step must be justified.
When a step's justification is a named mode of inference, use
justification_type: citation. The cited "result" is the mode of
inference itself.
PREMISES MUST BE EXPLICIT. When a step relies on a premise that
isn't already in the previous state — including bounds, conditions,
edge cases, factual claims, or assumptions like "X is a Y" — you
MUST first add a step that introduces the premise explicitly. That
added step needs its own justification. Do not bury premises in
justification text. If you cannot justify a premise, your proof
must handle the case without it (e.g., by considering all cases).
Use web search for anything you're even remotely unsure about.
Problem: Is a measurable share of published variant-caller accuracy an artefact of the regions GIAB v4.2.1 did not cover — re-run on GIAB HG002 v5.0q, is the F1 of published callers lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum?
Non-exhaustive sources that may help:
- GIAB HG002 benchmark v5.0q (T2T-HG002 v1.1 based; 99.9% of the diploid genome vs 85.2% for v4.2.1; +918 Mb newly benchmarked incl. chrX/Y) — https://www.nist.gov/programs-projects/genome-bottle
- Hansen N. et al., 'A complete diploid human genome benchmark for personalized genomics', Cell 189:4857-4875.e31 (2026) — https://doi.org/10.1016/j.cell.2026.06.016
- NIST reference materials RM 8391 / 8392 / 8393 / 8398 (HG002, trio, HG005, HG001) — https://www.nist.gov/programs-projects/genome-bottle
- GIAB/NIST stratification BEDs v3.6 incl. SegmentalDuplications, LowMappability, homopolymers (GRCh37/38, CHM13, HG002-Q100) — https://github.com/usnistgov/giab-stratifications
- Dwarshuis N. et al., stratifications description, Nat Commun 15 (2024) — https://doi.org/10.1038/s41467-024-53260-y
- GIAB CMRG benchmark v1.00 -- 273 of 395 challenging medically relevant genes (SMN1 etc.); Wagner J. et al., Nat Biotechnol (2022) — https://doi.org/10.1038/s41587-021-01158-1
- nf-core/sarek v3.10.0 'Aktse' (12 Aug 2026), MIT — https://nf-co.re/sarek
- RTG Tools / vcfeval v3.13 (BSD-2) -- actively maintained; hap.py v0.3.15 (2021) -- dormant but still the GA4GH reference implementation — https://github.com/RealTimeGenomics/rtg-tools
- Olson N.D. et al., 'Variant calling and benchmarking in an era of complete human genome sequences', Nat Rev Genet 24:464-483 (2023) — https://doi.org/10.1038/s41576-023-00590-0
- Qin Q. & Li H., SV calling in low-complexity regions vs HG002-Q100 v1.1; 77.3-91.3% of erroneous SV calls fall in LCRs, GigaScience (2025) — https://doi.org/10.1093/gigascience/giaf154
Solution: ## Conclusion
**The available evidence says the old benchmark materially overstated genome-wide performance, but a defensible “lower F1 in new versus old regions, per GIAB stratum” result has not yet been published or demonstrated by the cited sources.** A fresh, paired `hap.py`/`vcfeval` analysis is required.
GIAB now treats HG002 v5.0q as replacing v4.2.1, but NIST still describes the accompanying v5 benchmark manuscript as forthcoming. Also, the often-quoted **99.9% versus 85.2%** comparison concerns high-confidence coverage of the *diploid genome benchmark* after excluding rDNA—not directly the fraction of GRCh38’s hap.py small-variant benchmark BED. ([nist.gov](https://www.nist.gov/programs-projects/genome-bottle))
### What can already be measured
Sentieon published paired error counts for several frozen HG002 Illumina callsets against v4.2.1 and v5.0q:
| Caller | FP+FN, v4.2.1 | FP+FN, v5.0q | Increase |
|---|---:|---:|---:|
| DNAseq | 69,247 | 236,018 | 3.41× |
| DNAscope | 27,149 | 122,304 | 4.50× |
| DNAscope Pangenome | 5,605 | 58,165 | 10.38× |
| DRAGEN 4.5 | 6,451 | 78,860 | 12.22× |
These sums are recomputed from the four reported categories—SNV FP/FN and indel FP/FN. ([sentieon.com](https://www.sentieon.com/wp-content/uploads/2026/06/Figure1-1.jpg))
Thus, **a measurable share of the very small published error totals was conditional on what v4.2.1 omitted**. The effect is not marginal: v5.0q exposes tens to hundreds of thousands of additional errors in these examples. Hansen et al. independently show why this occurs: the new benchmark adds about 701.4 Mb of autosomal sequence plus 216.8 Mb of sex-chromosome sequence and includes duplicated, structurally polymorphic and repetitive regions systematically underrepresented before. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913/?utm_source=openai))
But those totals do **not** answer the requested per-stratum F1 question:
1. FP+FN does not determine F1 without TP counts.
2. v5.0q changes truth calls as well as benchmark coverage.
3. It adds X/Y, confounding “new region” with chromosome composition.
4. Published v5 stratified analyses generally compare callers across strata, not the same stratum split into `v4-covered` and `new-in-v5` portions. ([biorxiv.org](https://www.biorxiv.org/content/10.64898/2026.04.22.720142v1.full?utm_source=openai))
## Correct paired re-analysis
For each reference and caller, define both partitions using the **v5.0q truth and benchmark BED**:
\[
R_{\rm old}=B_{v5}\cap B_{v4.2.1}
\]
\[
R_{\rm new}=B_{v5}\setminus B_{v4.2.1}.
\]
For every GIAB v3.6 stratum \(S_j\):
\[
R_{{\rm old},j}=R_{\rm old}\cap S_j,\qquad
R_{{\rm new},j}=R_{\rm new}\cap S_j.
\]
Benchmark the identical frozen caller VCF twice against v5.0q:
```bash
hap.py HG002_GRCh38_v5.0q_truth.vcf.gz CALLER.vcf.gz \
-f old_in_stratum.bed \
-r GRCh38_GIAB.fa \
--engine vcfeval --gender male \
-o caller.old.stratum
hap.py HG002_GRCh38_v5.0q_truth.vcf.gz CALLER.vcf.gz \
-f new_in_stratum.bed \
-r GRCh38_GIAB.fa \
--engine vcfeval --gender male \
-o caller.new.stratum
```
Then calculate separately for SNVs and indels:
\[
F1=\frac{2TP}{2TP+FP+FN},\qquad
\Delta F1_j=F1_{{\rm new},j}-F1_{{\rm old},j}.
\]
The analysis should separate:
- newly covered autosomal regions;
- chromosome X non-PAR;
- chromosome Y non-PAR;
- PAR;
- all sex chromosomes combined.
Otherwise, an apparent new-region penalty could simply reflect the total absence of X/Y from v4.2.1.
Block-bootstrap genomic intervals—rather than individual variants—to obtain confidence intervals, since errors cluster in repeats. Strata with too few truth variants should be reported as underpowered rather than assigned unstable F1 values.
## Expected—but not yet measured here—stratum pattern
The evidence supports the following directional hypotheses:
| Stratum | Expected new-region F1 |
|---|---|
| Segmental duplications | Substantially lower |
| Low mappability | Substantially lower |
| Satellites/centromeric repeats | Substantially lower |
| Long tandem repeats | Lower, especially indels |
| Long homopolymers | Lower, strongly technology-dependent |
| `NotInAllDifficult` | Small or possibly no material decrease |
| Low/high GC | Caller- and library-dependent |
| X/Y and PAR | Must be reported separately |
GIAB’s stratification work already finds performance highest outside difficult regions and diagnoses very poor results in improperly handled PAR regions; Hansen et al. also report reduced consensus quality when newly accessible satellite-rich sequence is included. ([nature.com](https://www.nature.com/articles/s41467-024-53260-y?utm_source=openai))
**Bottom line:** **yes at the aggregate error-count level; probably yes for most repeat/mappability strata; but “yes per stratum” remains unverified until v5.0q is benchmarked with complementary old/new masks using the same v5 truth.** Reporting full-v5 versus v4.2.1 F1 alone is not sufficient to establish that result.
Formalize this into a proof, or reject if it has errors.
{
"action": {
"type": "search",
"queries": [
"site:nist.gov GIAB HG002 v5.0q v4.2.1 99.9% 85.2%",
"site:github.com/usnistgov HG002 v4.2.1 benchmark chrX GRCh38",
"site:sentieon.com v5.0q HG002 Figure 1 DNAseq DNAscope DRAGEN 4.5",
"doi 10.1016/j.cell.2026.06.016 Hansen complete diploid human genome benchmark"
],
"query": "site:nist.gov GIAB HG002 v5.0q v4.2.1 99.9% 85.2%"
}
}{
"action": {
"type": "open_page",
"url": "https://www.sentieon.com/?page_id=931"
}
}{
"action": {
"type": "open_page",
"url": "https://www.sentieon.com/?page_id=931"
}
}{
"action": {
"type": "search",
"queries": [
"HG002 v5.0q README hap.py vcfeval benchmark instructions GRCh38",
"\"v5.0q\" \"hap.py\" HG002",
"\"HG002_GRCh38_v5.0q\" truth vcf bed",
"site:biorxiv.org 2025.09.21.677443 benchmark v5.0q stratification F1"
],
"query": "HG002 v5.0q README hap.py vcfeval benchmark instructions GRCh38"
}
}{
"summary": [
"**Evaluating benchmarking flaws**\n\nI’m noticing a critical flaw in the approach of benchmarking identical VCF files against v5.0q using old and new BED when considering hap.py's -f confident regions. There might be issues with false positives outside of those regions. For correctness at boundaries, I'll need tools like bedtools and may have to restrict to internal regions. The main concern is that the question of \"published callers\" is too broad; vendor charts aren’t peer-reviewed and don't effectively represent accuracy.",
"**Analyzing errors and benchmarks**\n\nI’m thinking about how if I combine the same old regions with new ones, errors in v5 that are counted minus old ones can’t be specifically assigned to new truth revisions. It's indicating changes without stating the omission clearly. This creates a logical contradiction that's problematic. Also, there's a discrepancy between \"99.9%\" claimed and the Cell abstract stating 99.4%. The user cites 99.9 as well, so I’ll search the paper fully to clarify any benchmarks."
]
}{
"action": {
"type": "search",
"queries": [
"site:pmc.ncbi.nlm.nih.gov/articles/PMC13456413 99.9 85.2 rDNA v4.2.1",
"\"85.2%\" \"v4.2.1\" HG002 v5.0q",
"\"99.9%\" \"diploid genome\" HG002 benchmark",
"\"918.2 Mb\" HG002 benchmark v5.0q"
],
"query": "site:pmc.ncbi.nlm.nih.gov/articles/PMC13456413 99.9 85.2 rDNA v4.2.1"
}
}{
"summary": [
"**Evaluating solution accuracy**\n\nI’m looking into the clarification about the diploid benchmark, specifically the differences between 99.9 and 85.2. The paper mentions a benchmark of 99.4 with no detectable errors, but acknowledges 15.3 as absent, hinting at a previous figure of 84.7. Each aspect appears to raise critical questions. I should provide an exact JSON schema for output and list the substantial errors for clarity. The main reject points need detailing, including the outdated nature of the current solution and the invalidity of the aggregate error count inference.",
"**Clarifying F1 calculations**\n\nI’m considering how F1 calculations handle true positives (TP) in relation to hap.py metrics, which seem to define QUERY.TP and TRUTH.TP differently. This difference could mean that the formula for 2TP might not truly indicate a single TP if representation varies. In hap.py, precision and recall TPs may differ due to complex variants. It’s essential to check the official documentation for hap.py metrics to confirm these details. This distinction appears quite important!"
]
}{
"action": {
"type": "search",
"queries": [
"site:github.com/Illumina/hap.py \"TRUTH.TP\" \"QUERY.TP\" F1",
"site:github.com/ga4gh benchmarking-tools precision recall TRUTH.TP QUERY.TP",
"hap.py summary.csv METRIC.F1_Score TRUTH.TP QUERY.TP"
],
"query": "site:github.com/Illumina/hap.py \"TRUTH.TP\" \"QUERY.TP\" F1"
}
}{
"summary": [
"**Clarifying JSON output**\n\nI need to remember that formula simplification only works if the true positives (TP) are the same. In hap.py, queries and truth TPs can differ, which means the solution I found, suggesting F1=2TP, is erroneous unless I'm using a generic variant-level match. I definitely need to reject that. I want the output to be exact JSON, without markdown, and my reject reason should cover everything mentioned. I need to ensure a valid JSON output too."
]
}{
"action": "reject",
"proof": {
"initial_state": [],
"steps": []
},
"reject_reason": "The solution contains substantive factual and methodological errors. First, it says the v5 benchmark manuscript is “forthcoming,” but Hansen et al. was published in Cell on August 6, 2026. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913?utm_source=openai)) Second, the larger FP+FN totals under full v5.0q do not establish that any measurable share of prior accuracy was an artefact of omitted regions: v5.0q changes both the evaluated regions and the truth calls, and raw error totals also increase with the number of assessed bases and variants. Without partitioning v5.0q results into old and newly benchmarked regions, the extra errors cannot be attributed specifically to newly covered regions. Third, the stated formula F1=2TP/(2TP+FP+FN) is not generally valid for hap.py output because hap.py separately reports TRUTH.TP for recall and QUERY.TP for precision; F1 should be taken as the harmonic mean of METRIC.Recall and METRIC.Precision. ([github.com](https://github.com/Illumina/hap.py/blob/master/doc/happy.md?utm_source=openai)) Therefore the aggregate “yes” is unsupported, even though the proposed paired old/new analysis is broadly the right way to answer the question."
}You are one role in an automated verified-reasoning pipeline. Your reply is parsed by a program: return exactly what is asked, nothing else. You have no shell or filesystem.
You write the literature-search queries for one verification run, from its
digest: the question (a research proposal's hypothesis), the outcome, the
proof, and every audited claim with the checker's reason. You do not search.
An engine runs every query you return against OpenAlex (title, abstract and
full text of ~250M works) and records the exact URL, the total match count
and the top hits; the adjudicator reads those results, not your opinion.
Return one `searches` entry per claim worth testing:
- every absence claim the run relied on or failed on ("no study reports X",
"X has not been measured", "no benchmark covers Y"), in the digest's words;
- the proposal's question itself: has it already been answered?
For each, `queries` is a ladder from broad to narrow, as many rungs as the
vocabulary needs. The broadest rung names the topic and must plausibly match
published work; each narrower rung adds a condition the claim turns on.
Cover the field's synonyms and named artefacts (tools, datasets, benchmarks,
versions) across rungs rather than inside one query. A ladder whose broad
rung matches nothing is uncalibrated and proves nothing, so prefer several
plain rungs to one long one.
Query syntax: whole words, stemmed; AND / OR / NOT (uppercase) and
parentheses; "double quotes" for phrases. `from_date` / `to_date` bound the
publication date (YYYY-MM-DD; "" for unbounded). `issns`: journal ISSNs to
restrict to, only when the claim names venues; otherwise [].
QUESTION: Is a measurable share of published variant-caller accuracy an artefact of the regions GIAB v4.2.1 did not cover — re-run on GIAB HG002 v5.0q, is the F1 of published callers lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum? Non-exhaustive sources that may help: - GIAB HG002 benchmark v5.0q (T2T-HG002 v1.1 based; 99.9% of the diploid genome vs 85.2% for v4.2.1; +918 Mb newly benchmarked incl. chrX/Y) — https://www.nist.gov/programs-projects/genome-bottle - Hansen N. et al., 'A complete diploid human genome benchmark for personalized genomics', Cell 189:4857-4875.e31 (2026) — https://doi.org/10.1016/j.cell.2026.06.016 - NIST reference materials RM 8391 / 8392 / 8393 / 8398 (HG002, trio, HG005, HG001) — https://www.nist.gov/programs-projects/genome-bottle - GIAB/NIST stratification BEDs v3.6 incl. SegmentalDuplications, LowMappability, homopolymers (GRCh37/38, CHM13, HG002-Q100) — https://github.com/usnistgov/giab-stratifications - Dwarshuis N. et al., stratifications description, Nat Commun 15 (2024) — https://doi.org/10.1038/s41467-024-53260-y - GIAB CMRG benchmark v1.00 -- 273 of 395 challenging medically relevant genes (SMN1 etc.); Wagner J. et al., Nat Biotechnol (2022) — https://doi.org/10.1038/s41587-021-01158-1 - nf-core/sarek v3.10.0 'Aktse' (12 Aug 2026), MIT — https://nf-co.re/sarek - RTG Tools / vcfeval v3.13 (BSD-2) -- actively maintained; hap.py v0.3.15 (2021) -- dormant but still the GA4GH reference implementation — https://github.com/RealTimeGenomics/rtg-tools - Olson N.D. et al., 'Variant calling and benchmarking in an era of complete human genome sequences', Nat Rev Genet 24:464-483 (2023) — https://doi.org/10.1038/s41576-023-00590-0 - Qin Q. & Li H., SV calling in low-complexity regions vs HG002-Q100 v1.1; 77.3-91.3% of erroneous SV calls fall in LCRs, GigaScience (2025) — https://doi.org/10.1093/gigascience/giaf154 OUTCOME: Declined budget_exhausted DETAIL: solver budget spent NO PROOF WAS JUDGED — the formalizer rejected the solver's answer: The solution contains substantive factual and methodological errors. First, it says the v5 benchmark manuscript is “forthcoming,” but Hansen et al. was published in Cell on August 6, 2026. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913?utm_source=openai)) Second, the larger FP+FN totals under full v5.0q do not establish that any measurable share of prior accuracy was an artefact of omitted regions: v5.0q changes both the evaluated regions and the truth calls, and raw error totals also increase with the number of assessed bases and variants. Without partitioning v5.0q results into old and newly benchmarked regions, the extra errors cannot be attributed specifically to newly covered regions. Third, the stated formula F1=2TP/(2TP+FP+FN) is not generally valid for hap.py output because hap.py separately reports TRUTH.TP for recall and QUERY.TP for precision; F1 should be taken as the harmonic mean of METRIC.Recall and METRIC.Precision. ([github.com](https://github.com/Illumina/hap.py/blob/master/doc/happy.md?utm_source=openai)) Therefore the aggregate “yes” is unsupported, even though the proposed paired old/new analysis is broadly the right way to answer the question. TOOL ACTIVITY: adjudicator:web_search x1, formalizer:web_search x6, solver:code_interpreter x1, solver:web_search x17 STATS: 3 calls, 154.0s
{
"searches": [
{
"claim": "Has it already been shown that, when published variant callers are re-run on GIAB HG002 v5.0q, their F1 is lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum?",
"queries": [
"variant caller accuracy benchmarking GIAB HG002",
"variant calling benchmark difficult genomic regions HG002",
"variant caller F1 GIAB stratifications HG002",
"HG002 benchmark segmental duplications low mappability homopolymers variant calling",
"\"GIAB v4.2.1\" variant caller accuracy",
"\"HG002 v5.0q\" variant calling benchmark",
"\"T2T-HG002\" variant caller benchmarking",
"\"newly benchmarked regions\" HG002 variant calling",
"(\"GIAB v4.2.1\" OR \"HG002 v4.2.1\") AND (\"HG002 v5.0q\" OR \"T2T-HG002\")",
"(\"HG002 v5.0q\" OR \"T2T-HG002\") AND F1 AND stratification",
"(\"HG002 v5.0q\" OR \"T2T-HG002\") AND variant caller AND (\"segmental duplications\" OR \"low mappability\" OR homopolymers)",
"(\"GIAB v4.2.1\" AND \"HG002 v5.0q\") AND (old OR previous) AND (new OR newly) AND regions"
],
"from_date": "",
"to_date": "",
"issns": []
}
]
}You are one role in an automated verified-reasoning pipeline. Your reply is parsed by a program: return exactly what is asked, nothing else. You have no shell or filesystem. Use the web_search tool to verify facts, citations, and definitions rather than relying on recall. Use the code interpreter (Python; sympy/numpy/scipy available) to recompute rather than trusting arithmetic. You give the screening verdict on one research-proposal question, using the digest of a verification run: the question (the proposal's hypothesis), the outcome, the proof, and every audited claim with the checker's full reason. `verdict` — is the proposal's question genuinely open and worth a human reviewer's time? - yes: the run establishes that the question is real, open, and well-posed — a certified "not settled by the reviewed evidence", or a checker-verified absence of the result the proposal would supply. - no: the run shows it is not a fundable open question — already settled by the literature, ill-posed, or its supporting claims collapse on checkable facts. - maybe: the run leaves specific uncertainties only a human can resolve. If openness rests on something the run did not check — whether the analysis is already published, whether the data exists — that is maybe, with the check as a review item, not yes. The digest may end with a LITERATURE SEARCH section: engine-run OpenAlex queries with their total match counts, per claim, broad to narrow; a rung with at most 10 matches lists them (title, year, venue, doi). Read it as evidence, not as a verdict. Hits whose titles answer the proposal's question support no (already settled). Zero hits on the narrow rungs of a calibrated ladder (its broad rungs matched) support yes for that claim's absence. An uncalibrated ladder establishes nothing, and a FAILED rung is unknown, not zero. Name the query or hit you rely on. `explanation`: for yes or no, 2-4 sentences grounded only in the digest. For maybe, one sentence naming the core uncertainty. `review_items`: for maybe only — 2 to 6 concrete questions or checks for the human reviewer, each answerable and each tied to something in the digest. Empty for yes and no.
QUESTION:
Is a measurable share of published variant-caller accuracy an artefact of the regions GIAB v4.2.1 did not cover — re-run on GIAB HG002 v5.0q, is the F1 of published callers lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum?
Non-exhaustive sources that may help:
- GIAB HG002 benchmark v5.0q (T2T-HG002 v1.1 based; 99.9% of the diploid genome vs 85.2% for v4.2.1; +918 Mb newly benchmarked incl. chrX/Y) — https://www.nist.gov/programs-projects/genome-bottle
- Hansen N. et al., 'A complete diploid human genome benchmark for personalized genomics', Cell 189:4857-4875.e31 (2026) — https://doi.org/10.1016/j.cell.2026.06.016
- NIST reference materials RM 8391 / 8392 / 8393 / 8398 (HG002, trio, HG005, HG001) — https://www.nist.gov/programs-projects/genome-bottle
- GIAB/NIST stratification BEDs v3.6 incl. SegmentalDuplications, LowMappability, homopolymers (GRCh37/38, CHM13, HG002-Q100) — https://github.com/usnistgov/giab-stratifications
- Dwarshuis N. et al., stratifications description, Nat Commun 15 (2024) — https://doi.org/10.1038/s41467-024-53260-y
- GIAB CMRG benchmark v1.00 -- 273 of 395 challenging medically relevant genes (SMN1 etc.); Wagner J. et al., Nat Biotechnol (2022) — https://doi.org/10.1038/s41587-021-01158-1
- nf-core/sarek v3.10.0 'Aktse' (12 Aug 2026), MIT — https://nf-co.re/sarek
- RTG Tools / vcfeval v3.13 (BSD-2) -- actively maintained; hap.py v0.3.15 (2021) -- dormant but still the GA4GH reference implementation — https://github.com/RealTimeGenomics/rtg-tools
- Olson N.D. et al., 'Variant calling and benchmarking in an era of complete human genome sequences', Nat Rev Genet 24:464-483 (2023) — https://doi.org/10.1038/s41576-023-00590-0
- Qin Q. & Li H., SV calling in low-complexity regions vs HG002-Q100 v1.1; 77.3-91.3% of erroneous SV calls fall in LCRs, GigaScience (2025) — https://doi.org/10.1093/gigascience/giaf154
OUTCOME: Declined budget_exhausted
DETAIL: solver budget spent
NO PROOF WAS JUDGED — the formalizer rejected the solver's answer:
The solution contains substantive factual and methodological errors. First, it says the v5 benchmark manuscript is “forthcoming,” but Hansen et al. was published in Cell on August 6, 2026. ([pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/42561913?utm_source=openai)) Second, the larger FP+FN totals under full v5.0q do not establish that any measurable share of prior accuracy was an artefact of omitted regions: v5.0q changes both the evaluated regions and the truth calls, and raw error totals also increase with the number of assessed bases and variants. Without partitioning v5.0q results into old and newly benchmarked regions, the extra errors cannot be attributed specifically to newly covered regions. Third, the stated formula F1=2TP/(2TP+FP+FN) is not generally valid for hap.py output because hap.py separately reports TRUTH.TP for recall and QUERY.TP for precision; F1 should be taken as the harmonic mean of METRIC.Recall and METRIC.Precision. ([github.com](https://github.com/Illumina/hap.py/blob/master/doc/happy.md?utm_source=openai)) Therefore the aggregate “yes” is unsupported, even though the proposed paired old/new analysis is broadly the right way to answer the question.
TOOL ACTIVITY: adjudicator:web_search x1, formalizer:web_search x6, solver:code_interpreter x1, solver:web_search x17
LITERATURE SEARCH (engine-witnessed, openalex, as of 2026-09-02; total matches per query, queries broad -> narrow):
- CLAIM: Has it already been shown that, when published variant callers are re-run on GIAB HG002 v5.0q, their F1 is lower in the newly benchmarked regions than in the v4.2.1 regions, per stratum?
470 hits: variant caller accuracy benchmarking GIAB HG002
492 hits: variant calling benchmark difficult genomic regions HG002
76 hits: variant caller F1 GIAB stratifications HG002
131 hits: HG002 benchmark segmental duplications low mappability homopolymers variant calling
61 hits: "GIAB v4.2.1" variant caller accuracy
0 hits: "HG002 v5.0q" variant calling benchmark
23 hits: "T2T-HG002" variant caller benchmarking
0 hits: "newly benchmarked regions" HG002 variant calling
6 hits: ("GIAB v4.2.1" OR "HG002 v4.2.1") AND ("HG002 v5.0q" OR "T2T-HG002")
· 2026 A complete diploid human genome benchmark for personalized genomics (Cell) https://doi.org/10.1016/j.cell.2026.06.016
· 2024 Personalized pangenome references (Nature Methods) https://doi.org/10.1038/s41592-024-02407-2
· 2024 StratoMod: predicting sequencing and variant calling errors with interpretable machine learning (Communications Biology) https://doi.org/10.1038/s42003-024-06981-1
6 hits: ("HG002 v5.0q" OR "T2T-HG002") AND F1 AND stratification
· 2026 A complete diploid human genome benchmark for personalized genomics (Cell) https://doi.org/10.1016/j.cell.2026.06.016
· 2026 Benchmarking of sequencing technologies defines optimal strategies for genetic variants detection in a human genome (Genome biology) https://doi.org/10.1186/s13059-026-04048-4
· 2026 The promise of long-read RNA-seq: reducing bias in analyses of allele imbalance (NAR Genomics and Bioinformatics) https://doi.org/10.1093/nargab/lqag071
17 hits: ("HG002 v5.0q" OR "T2T-HG002") AND variant caller AND ("segmental duplications" OR "low mappability" OR homopolymers)
0 hits: ("GIAB v4.2.1" AND "HG002 v5.0q") AND (old OR previous) AND (new OR newly) AND regions
STATS: 3 calls, 154.0s
{
"verdict": "yes",
"explanation": "The calibrated literature ladder found broad HG002 benchmarking literature but zero matches for the exact old-versus-new regional comparison, including the query “(\"GIAB v4.2.1\" AND \"HG002 v5.0q\") AND (old OR previous) AND (new OR newly) AND regions.” None of the listed hits has a title indicating that published callers’ F1 was compared between v4.2.1-covered and newly benchmarked regions per stratum. The digest also confirms that this partitioned analysis is methodologically appropriate, while aggregate error totals alone cannot answer it.",
"review_items": []
}