Characterizing polymorphic inversions in human genomes by single-cell sequencing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying genomic features that differ between individuals and cells can help uncover the functional variants that drive phenotypes and disease susceptibilities. For this, single-cell studies are paramount, as it becomes increasingly clear that the contribution of rare but functional cellular subpopulations is important for disease prognosis, management, and progression. Until now, studying these associations has been challenged by our inability to map structural rearrangements accurately and comprehensively. To overcome this, we coupled single-cell sequencing of DNA template strands (Strand-seq) with custom analysis software to rapidly discover, map, and genotype genomic rearrangements at high resolution. This allowed us to explore the distribution and frequency of inversions in a heterogeneous cell population, identify several polymorphic domains in complex regions of the genome, and locate rare alleles in the reference assembly. We then mapped the entire genomic complement of inversions within two unrelated individuals to characterize their distinct inversion profiles and built a nonredundant global reference of structural rearrangements in the human genome. The work described here provides a powerful new framework to study structural variation and genomic heterogeneity in single-cell samples, whether from individuals for population studies or tissue types for biomarker discovery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it