Comparative Genomics, Marker Density and Statistical Analysis of Chromosome Rearrangements
Bibliographic record
Abstract
Estimates of the number of chromosomal breakpoints that have arisen (e.g., by translocation and inversion) in the evolutionary past between two species and their common ancestor can be made by comparing map positions of marker loci. Statistical methods for doing so are based on a random-breakage model of chromosomal rearrangement. The model treats all modes of chromosome rearrangement alike, and it assumes that chromosome boundaries and breakpoints are distributed randomly along a single genomic interval. Here we use simulation and numerical analysis to test the validity of these model assumptions. Mean estimates of numbers of breakpoints are close to those expected under the random-breakage model when marker density is high relative to the amount of chromosomal rearrangement and when rearrangements occur by translocation alone. But when marker density is low relative to the number of chromosomes, and when rearrangements occur by both translocation and inversion, the number of breakpoints is underestimated. The underestimate arises because rearranged segments may contain markers, yet the rearranged segments may, nevertheless, be undetected. Variances of the estimate of numbers of breakpoints decrease rapidly as markers are added to the comparative maps, but are less influenced by the number or type of chromosomal rearrangement separating the species. Variances obtained with simulated genomes comprised of chromosomes of equal length are substantially lower than those obtained when chromosome size is unconstrained. Statistical power for detecting heterogeneity in the rate of chromosomal rearrangement is also investigated. Results are interpreted with respect to the amount of marker information required to make accurate inferences about chromosomal evolution.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".