Genome survey sequencing of <i>Dioscorea zingiberensis</i>
Bibliographic record
Abstract
Dioscorea zingiberensis (Dioscoreceae) is the main plant source of diosgenin (steroidal sapogenins), the precursor for the production of steroid hormones in the pharmaceutical industry. Despite its large economic value, genomic information of the genus Dioscorea is currently unavailable. Here, we present an initial survey of the D. zingiberensis genome performed by next-generation sequencing technology together with a genome size investigation inferred by flow cytometry. The whole genome survey of D. zingiberensis generated 31.48 Gb of sequence data with approximately 78.70× coverage. The estimated genome size is 800 Mb, with a high level of heterozygosity based on K-mer analysis. These reads were assembled into 334 288 contigs with a N50 length of 1079 bp, which were further assembled into 92 163 scaffolds with a total length of 173.46 Mb. A total of 4935 genes, 81 tRNAs, 69 rRNAs, and 661 miRNAs were predicted by the genome analysis, and 263 484 repeated sequences were obtained with 419 372 simple sequence repeats (SSRs). Among these SSRs, the mononucleotide repeat type was the most abundant (up to 54.60% of the total SSRs), followed by the dinucleotide (29.60%), trinucleotide (11.37%), tetranucleotide (3.53%), pentanucleotide (0.65%), and hexanucleotide (0.25%) repeat types. The 1C-value of D. zingiberensis was calibrated against Salvia miltiorrhiza and calculated as 0.87 pg (851 Mb) by flow cytometry, which was very close to the result of the genome survey. This is the first report of genome-wide characterization within this taxon.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".