Fraction of Informative Recombinations: A Heuristic Approach to Analyze Recombination Rates
Bibliographic record
Abstract
In this article we present a new heuristic approach (informative recombinations, InfRec) to analyze recombination density at the sequence level. InfRec is intuitive and easy and combines previously developed methods that (i) resolve genotypes into haplotypes, (ii) estimate the minimum number of recombinations, and (iii) evaluate the fraction of informative recombinations. We tested this approach in its sliding-window version on 117 genes from the SeattleSNPs program, resequenced in 24 African-Americans (AAs) and 23 European-Americans (EAs). We obtained population recombination rate estimates (rho(obs)) of 0.85 and 0.37 kb(-1) in AAs and EAs, respectively. Coalescence simulations indicated that these values account for both the recombinations and the gene conversions in the history of the sample. The intensity of rho(obs) varied considerably along the sequence, revealing the presence of recombination hotspots. Overall, we observed approximately 80% of recombinations in one-third and approximately 50% in only 10% of the sequence. InfRec performance, tested on published simulated and additional experimental data sets, was similar to that of other hotspot detection methods. Fast, intuitive, and visual, InfRec is not constrained by sample size limitations. It facilitates understanding data and provides a simple and flexible tool to analyze recombination intensity along the sequence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".