Whole-genome sequence of the Tibetan frog <i>Nanorana parkeri</i> and the comparative evolution of tetrapod genomes
Bibliographic record
Abstract
The development of efficient sequencing techniques has resulted in large numbers of genomes being available for evolutionary studies. However, only one genome is available for all amphibians, that of Xenopus tropicalis, which is distantly related from the majority of frogs. More than 96% of frogs belong to the Neobatrachia, and no genome exists for this group. This dearth of amphibian genomes greatly restricts genomic studies of amphibians and, more generally, our understanding of tetrapod genome evolution. To fill this gap, we provide the de novo genome of a Tibetan Plateau frog, Nanorana parkeri, and compare it to that of X. tropicalis and other vertebrates. This genome encodes more than 20,000 protein-coding genes, a number similar to that of Xenopus. Although the genome size of Nanorana is considerably larger than that of Xenopus (2.3 vs. 1.5 Gb), most of the difference is due to the respective number of transposable elements in the two genomes. The two frogs exhibit considerable conserved whole-genome synteny despite having diverged approximately 266 Ma, indicating a slow rate of DNA structural evolution in anurans. Multigenome synteny blocks further show that amphibians have fewer interchromosomal rearrangements than mammals but have a comparable rate of intrachromosomal rearrangements. Our analysis also identifies 11 Mb of anuran-specific highly conserved elements that will be useful for comparative genomic analyses of frogs. The Nanorana genome offers an improved understanding of evolution of tetrapod genomes and also provides a genomic reference for other evolutionary studies.
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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) | 0.003 | 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".