Sequencing of isolated sperm cells for direct haplotyping of a human genome
Bibliographic record
Abstract
There is increasing evidence that the phenotypic effects of genomic sequence variants are best understood in terms of variant haplotypes rather than as isolated polymorphisms. Haplotype analysis is also critically important for uncovering population histories and for the study of evolutionary genetics. Although the sequencing of individual human genomes to reveal personal collections of sequence variants is now well established, there has been slower progress in the phasing of these variants into pairs of haplotypes along each pair of chromosomes. Here, we have developed a distinct approach to haplotyping that can yield chromosome-length haplotypes, including the vast majority of heterozygous single-nucleotide polymorphisms (SNPs) in an individual human genome. This approach exploits the haploid nature of sperm cells and employs a combination of genotyping and low-coverage sequencing on a short-read platform. In addition to generating chromosome-length haplotypes, the approach can directly identify recombination events (averaging 1.1 per chromosome) with a median resolution of <100 kb.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".