Bibliographic record
Abstract
The OurDNA dataset is composed of harmonised, aggregated genome and exome sequences from the OurDNA program and provides the foundational reference set used by the OurDNA browser. The OurDNA program is a flagship initiative of the Centre for Population Genomics to increase the genomic representation of Australian multicultural communities. The OurDNA program aims to aggregate and share genetic variation data from over 20,000 Australians, including 8,000 new high-quality whole genome sequences from participants from genomically underrepresented groups recruited following participatory community engagement. The OurDNA Browser is a resource intended for clinicians and researchers with formal training in genetics and genomics who understand the limitations of population genetic data. Use of the dataset is subject to conditions of use as outlined in OurDNA browser policies. The OurDNA dataset v1 (GRCh38) includes 12,882 individuals: 10,671 exomes 2,211 genomes Short variants Total SNVs: 57,322,471 Total INDELs: 4,567,608 Variant type counts Synonymous: 719,413 Missense: 1,321,931 Nonsense: 35,735 Frameshift: 36,991 Canonical splice site: 33,237 Versioned, aggregate data are available for download. Download instructions are provided on the OurDNA browser. Methods The OurDNA dataset contains individuals sequenced using a mix of exome and genome capture methods and sequencing chemistries, so coverage varies between individuals and across sites. This variation in coverage is incorporated into the variant frequency calculations for each variant. Data were QCed and analyzed using the Hail open-source framework for scalable genetic analysis. All of the raw data from contributing projects and the OurDNA project have been (re)processed through equivalent pipelines to increase consistency across projects. Short-read whole genome sequencing data was processed according to the DRAGEN-GATK Best Practices guidelines. This includes alignment to GRCh38 using the open-source DRAGEN mapper (DRAGMAP, v1.3.0), and variant calling with GATK v4.2.6.1 HaplotypeCaller to discover single-nucleotide variants (SNVs) and insertion-deletions (indels). All samples were aggregated using the hail gVCF Combiner, and then sample and variant quality control was performed on the joint call set in line with gnomAD best practices. Funding Garvan Institute of Medical Research (https://ror.org/01b3dvp57) and Murdoch Children’s Research Institute (https://ror.org/048fyec77) contribute to the development of this resource via their significant funding support for the Centre for Population Genomics, enabled through the generosity of donors. Funding for this research has also been provided by the Australian Government’s Medical Research Future Fund (MRFF) grant 2015969 (CIA Daniel MacArthur; 2022-2027) from the Genomics Health Futures Mission and by the National Health and Medical Research Council (NHMRC, https://ror.org/011kf5r70) investigator grant 2009982 (CIA Daniel MacArthur; 2022-2026). The contents of this published material are solely the responsibility of the authors and do not reflect the views of the Commonwealth of Australia or the NHMRC.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.095 |
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".