Artificially designed hybrids facilitate efficient generation of high-resolution linkage maps
Bibliographic record
Abstract
When sequencing eukaryotic genomes, linkage maps are indispensable for building scaffolds to assemble and/or to validate chromosomes. However, current approaches to constructing linkage maps are limited by marker density and cost-effectiveness, especially for wild organisms. We have now devised a new strategy based on artificially generated hybrid organisms to acquire ultrahigh-density genomic markers at reduced cost and build highly accurate linkage maps. We have also developed the novel analysis pipeline Scaffold Extender with Low Depth Linkage Analysis (SELDLA) for data processing to generate linkage maps and draft genomes. Using SELDLA, linkage maps and improved genomes for two species of pufferfish, Takifugu rubripes and Takifugu stictonotus, were obtained simultaneously. The strategy is applicable to a wide range of sexually reproducing organisms, and could, therefore, accelerate the whole genome analysis of various organisms including fish, mollusks, amphibians, insects, plants, and even mammals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".