Synteny Enabled Upgrade of the Galapagos Giant Tortoise Genome Improves Inferences of Runs of Homozygosity
Bibliographic record
Abstract
ABSTRACT The utility and importance of whole‐genome sequences are recognized across various fields, including evolution and conservation. However, for some taxa, like extinct species, using methods to generate contiguous genomes that rely on high‐quality DNA is impossible. In such cases, an alternative may be to employ synteny‐based methods using a genome from a closely related taxon to generate more complete genomes. Here we update the reference genome for the Pinta Island Galapagos giant tortoise ( Chelonoidis abingdonii ) without conducting additional sequencing through rescaffolding against the most closely related chromosome‐level genome assembly, the Aldabra giant tortoise ( Aldabrachelys gigantea ). This effort resulted in a much more contiguous genome, CheloAbing_2.0, with an N50 that is two orders of magnitude longer and large reductions in L50 and the number of gaps. We then examined the impact of the CheloAbing_2.0 genome on estimates of runs of homozygosity (ROH) using genome resequencing data from 37 individual Galapagos giant tortoises from the 13 extant lineages to test the mechanisms by which a fragmented assembly may over‐ or underestimate the number and extent of ROH. The use of CheloAbing_2.0 resulted in individual estimates of inbreeding, including ROH proportion (F ROH ), number (N ROH ), and cumulative length (S ROH ), that were statistically different from those derived from the earlier genome assembly. This improved genome will serve as a resource for future efforts focusing on the ecology, evolution, and conservation of this species group. More broadly, our results highlight that synteny‐based scaffolding is promising for generating contiguous genomes without needing additional data types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".