Construction of a chromosome-level Japanese stickleback species genome using ultra-dense linkage analysis with single-cell sperm sequencing
Bibliographic record
Abstract
Abstract It is still difficult to construct the genomes of higher organisms as their genome sequences must be extended to the length of the chromosome by linkage analysis. In this study, we attempted to provide an innovative alternative to conventional linkage analysis by devising a method to genotype sperm using 10× Genomics single-cell genome sequencing libraries to generate a linkage map without interbreeding individuals. A genome was assembled using sperm from the Japanese stickleback Gasterosteus nipponicus, with single-cell genotyping yielding 1 864 430 very dense hetero-SNPs and an average coverage per sperm cell of 0.13×. In total, 1665 sperm were used, which is an order of magnitude higher than the number of recombinations used for conventional linkage analysis. We then improved the linkage analysis tool scaffold extender with low depth linkage analysis (SELDLA) to analyze the data according to the characteristics of the single-cell genotyping data. Finally, we were able to determine the chromosomal location (97.1%) and orientation (64.4%) of the contigs in the 456 Mb genome of G. nipponicus, sequenced using nanopores. This method promises to be a useful tool for determining the genomes of non-model organisms for which breeding systems have not yet been established by linkage analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".