A draft UAE-based Arab pangenome reference
Bibliographic record
Abstract
Pangenomes provide a robust and comprehensive portrayal of genetic diversity in humans, but Arab populations remain underrepresented. We present a preliminary UAE-based Arab Pangenome Reference (UPR) utilizing 53 individuals of diverse Arab ethnicities residing in the United Arab Emirates. We assembled nuclear and mitochondrial pangenomes using 35.27X high-fidelity long reads, 54.22X ultralong reads and 65.46X Hi-C reads. This approach yielded contiguous haplotype-phased de novo assemblies of exceptional quality, with an average N50 of 124.28 Mb. We discovered 111.96 million base pairs of previously uncharacterized euchromatic sequences absent from existing human pangenomes, the T2T-CHM13 and GRCh38 reference human genomes, and other public datasets. Moreover, we identified 8.94 million population-specific small variants and 235,195 structural variants within the Arab pangenome, not present in linear and pangenome references and public datasets. We detected 883 gene duplications, including the TATA-binding protein gene TAF11L5, which was uniquely duplicated across all Arab populations and that included 15.06% of genes associated with recessive diseases. By exploring the mitochondrial pangenome, we identified 1,436 bp of previously unreported sequences. Our study provides a valuable resource for future genetic research and genomic medicine initiatives in Arab population and other population with similar genetic backgrounds.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".