Ultra-High-Density Genetic Maps of Jatropha curcas × Jatropha integerrima and Anchoring Jatropha curcas Genome Assembly Scaffolds
Bibliographic record
Abstract
Genetic maps facilitate an understanding of genome organization and the mapping of genes and QTLs for traits of interest. Our objective was to develop a high-density genetic map of Jatropha and anchoring scaffolds from genome assemblies. We developed two ultra-high-density genetic linkage maps of Jatropha curcas × Jatropha intergerrima using a backcross (BC1) population using SNP, AFLP and SSR markers. First, SNPs were identified through genotyping-by-sequencing (GBS). The polymorphic SNPs were mapped to 3267 Jat_r4.5 scaffolds and 484 Wu_JatCur_1.0 scaffolds, and then these genomic scaffolds were mapped/anchored to the genetic linkage groups along with the AFLP and SSR markers for each genome assembly separately. We successfully mapped 7284 polymorphic SNPs, and 54 AFLP and SSR markers on 11 linkage groups using the Jat_r4.5 genomic scaffolds, resulting in a genome length of 1088 cM and an average marker interval of 0.71 cM. We mapped 7698 polymorphic SNPs, and 99 AFLP and SSR markers on 11 linkage groups using the Wu_JatCur_1.0 genomic scaffolds, resulting in a genome length of 870 cM and an average marker interval of 1.67 cM. The mapped SNPs were annotated to various regions of the genome, including exon, intron and intergenic regions. We developed two ultra-high-density linkage maps anchoring a high number of genome scaffolds to linkage groups, which provide an important resource for the structural and functional genomics as well as for molecular breeding of Jatropha while also serving as a framework for assembling and ordering whole genome scaffolds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".