Multi-tissue transcriptomes of caecilian amphibians highlight incomplete knowledge of vertebrate gene families
Bibliographic record
Abstract
RNA sequencing (RNA-seq) has become one of the most powerful tools to unravel the genomic basis of biological adaptation and diversity. Although challenging, RNA-seq is particularly promising for research on non-model, secretive species that cannot be observed in nature easily and therefore remain comparatively understudied. Among such animals, the caecilians (order Gymnophiona) likely constitute the least known group of vertebrates, despite being an old and remarkably distinct lineage of amphibians. Here, we characterize multi-tissue transcriptomes for five species of caecilians that represent a broad level of diversity across the order. We identified vertebrate homologous elements of caecilian functional genes of varying tissue specificity that reveal a great number of unclassified gene families, especially for the skin. We annotated several protein domains for those unknown candidate gene families to investigate their function. We also conducted supertree analyses of a phylogenomic dataset of 1,955 candidate orthologous genes among five caecilian species and other major lineages of vertebrates, with the inferred tree being in agreement with current views of vertebrate evolution and systematics. Our study provides insights into the evolution of vertebrate protein-coding genes, and a basis for future research on the molecular elements underlying the particular biology and adaptations of caecilian amphibians.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".