Ultra‐barcoding in cacao (<i>Theobroma</i> spp.; Malvaceae) using whole chloroplast genomes and nuclear ribosomal DNA
Bibliographic record
Abstract
PREMISE OF STUDY: To reliably identify lineages below the species level such as subspecies or varieties, we propose an extension to DNA-barcoding using next-generation sequencing to produce whole organellar genomes and substantial nuclear ribosomal sequence. Because this method uses much longer versions of the traditional DNA-barcoding loci in the plastid and ribosomal DNA, we call our approach ultra-barcoding (UBC). METHODS: We used high-throughput next-generation sequencing to scan the genome and generate reliable sequence of high copy number regions. Using this method, we examined whole plastid genomes as well as nearly 6000 bases of nuclear ribosomal DNA sequences for nine genotypes of Theobroma cacao and an individual of the related species T. grandiflorum, as well as an additional publicly available whole plastid genome of T. cacao. KEY RESULTS: All individuals of T. cacao examined were uniquely distinguished, and evidence of reticulation and gene flow was observed. Sequence variation was observed in some of the canonical barcoding regions between species, but other regions of the chloroplast were more variable both within species and between species, as were ribosomal spacers. Furthermore, no single region provides the level of data available using the complete plastid genome and rDNA. CONCLUSIONS: Our data demonstrate that UBC is a viable, increasingly cost-effective approach for reliably distinguishing varieties and even individual genotypes of T. cacao. This approach shows great promise for applications where very closely related or interbreeding taxa must be distinguished.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".