Structural Genomic Variation and Its Potential Role In Deer Speciation
Bibliographic record
Abstract
Speciation is a key driver of biodiversity and understanding its genomic underpinnings can be important for predicting and managing biodiversity. Structural variants (SVs) are large-scale (>50 bp) changes in the genome and have been implicated in adaptive divergence and reproductive isolation. We investigated the role of SVs in the speciation and divergence of two deer species (Odocoileus spp.) across their North American range. Using multiple long-read and a short-read datasets, our bioinformatics workflow revealed SVs and genomic features that were unique to each species. The majority of species-specific SVs were deletions and insertions suggesting that these variants may show higher likelihoods of fixation within populations. Further, while most SVs were intergenic, some genes were impacted, with 3 species-specific SVs showing signs of selection inferred from dN/dS. We also observed a reduced number of regulatory motifs found in fixed SVs compared to the rest of the genome. The SV-affected genes were often associated with reproduction and sensory adaptation, with such functions being relevant to fertility and deer biology and therefore providing insights into potential mechanisms leading to reproductive divergence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".