Shark and ray genome size estimation: methodological optimization for inclusive and controllable biodiversity genomics
Bibliographic record
Abstract
Estimate of nuclear DNA content serves as an independent tool for validating the completeness of whole genome sequences and investigating the among-species variation of genome sizes, but for some species, the requirement of fresh cells makes this tool highly inaccessible. Here we focused on elasmobranch species (sharks and rays), and using flow cytometry or quantitative PCR (qPCR), estimated the nuclear DNA contents of brownbanded bamboo shark, white spotted bamboo shark, zebra shark, small-spotted catshark, sandbar shark, slendertail lanternshark, basking shark, megamouth shark, red stingray, and ocellate spot skate. Our results revealed their genome sizes spanning from 3.18 pg (for ocellate spot skate) to 13.34 pg (for slendertail lanternshark), reflecting the huge variation of genome sizes already documented for elasmobranchs. Our qPCR-based method ‘sQuantGenome’ enabled accurate genome size estimation without using live cells, which has been a severe limitation with elasmobranchs. These findings and our methodology are expected to contribute to better understanding of the diversity of genome sizes in elasmobranchs even including species with limited availability of fresh tissue materials. It will also help validate the completeness of already obtained or anticipated whole genome sequences.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".