Telomere-to-Telomere Accurate and Gapless Korean Standard Reference Genome
Bibliographic record
Abstract
Abstract We present KOREF1-G-TTAGGA, the first Telomere-to-Telomere Accurate and Gapless Genome Assembly, standing as the Korean standard reference genome. The paternal and maternal haplotypes spanned 2.91 and 3.03 Gb. Genome-wide, at least 99.15% of assembled sequences were reliably haplotype-resolved, and over 95% remained accurate even in the most error-prone loci, including centromeric satellite arrays and segmental duplications. Rare-k-mer copy-number concordance within satellite arrays held up 98.9% and 98.8% per haplotype. Bionano optical maps fully spanned all canonical rDNA arrays on the five acrocentric chromosomes. The assembly quality index (AQI) of 99.77 and 99.69 exceeded the reference-quality threshold of 90, and more than 99.99% of gene and cCRE sequence was free of structural error. Both haplotypes further showed high base-level accuracy, with consensus quality value (QV) of 81.19 and 79.03, corresponding to one error per 131 and 80 Mb. KOREF1-G-TTAGGA is among the highest-quality East Asian telomere-to-telomere assemblies. It moreover anchors a decade-spanning multi-ome reference dataset for defining individual’s molecular states. Together, these resources define the personal referenceome as a foundation for individual biology and precision medicine, with the assemblies, annotations, and all multi-omic data openly available at https://koreanreference.org .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".