<i>De novo</i> transcriptome analysis of dermal tissue from the rough-skinned newt, <i>Taricha granulosa</i> , enables investigation of tetrodotoxin expression
Bibliographic record
Abstract
Abstract Background Tetrodotoxin (TTX) is a potent neurotoxin used in anti-predator defense by several aquatic species, including the rough-skinned newt, Taricha granulosa . While several possible biological sources of newt TTX have been investigated, mounting evidence suggests a genetic, endogenous origin. We present here a de novo transcriptome assembly and annotation of dorsal skin samples from the tetrodotoxin-bearing species T. granulosa , to facilitate the study of putative genetic mechanisms of TTX expression. Findings Approximately 211 million read pairs were assembled into 245,734 transcripts using the Trinity de novo assembly method. Of the assembled transcripts, we were able to annotate 34% by comparing them to databases of sequences with known functions, suggesting that many transcripts are unique to the rough-skinned newt. Our assembly has near-complete sequence information for an estimated 83% of genes based on Benchmarking Universal Single Copy Orthologs. We also utilized other comparative methods to assess the quality of our assembly. The T. granulosa assembly was compared with that of the Japanese fire-belly newt, Cynops pyrrhogaster , and they were found to share a total of 30,556 orthologous sequences (12.9% gene set). Conclusions We provide a reference assembly for Taricha granulosa that will enable downstream differential expression and comparative transcriptomics analyses. This publicly available transcriptome assembly and annotation dataset will facilitate the investigation of a wide range of questions concerning amphibian adaptive radiation, and the elucidation of mechanisms of tetrodotoxin defense in Taricha granulosa and other TTX-bearing species.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".