Evolutionary dynamics of abundant 7 bp satellites in the genome of <i>Drosophila virilis</i>
Bibliographic record
Abstract
Abstract The factors that drive the rapid changes in satellite DNA genomic composition we see in eukaryotes are not well understood. Drosophila virilis has one of the highest relative amounts of simple satellites of any organism that has been studied, with an estimated >40% of its genome composed of a few related 7 bp satellites. Here we use D. virilis as a model to understand technical biases affecting satellite sequencing and the evolutionary processes that drive satellite composition. By analyzing sequencing data from Illumina, PacBio, and Nanopore platforms, we identify platform-specific biases and suggest best practices for accurate characterization of satellites by sequencing. We use comparative genomics and cytogenetics to demonstrate that the highly abundant satellite family arose from a related satellite in the branch leading to the virilis phylad 4.5 - 11 million years ago before exploding in abundance in some species of the clade. The most abundant satellite is conserved in sequence and location in the pericentromeric region but has diverged widely in abundance among species, whereas the satellites nearest the centromere are rapidly turning over in sequence composition. By analyzing multiple strains of D. virilis , we saw that one centromere-proximal satellite is increasing in abundance along a geographical gradient while the other is contracting in an anti-correlated manner, suggesting ongoing conflicts at the centromere. In conclusion, we illuminate several key attributes of satellite evolutionary dynamics that we hypothesize to be driven by processes like selection, meiotic drive, and constraints on satellite sequence and abundance.
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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 | 0.000 |
| 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".