Comparison of methodological approaches to the study of young sex chromosomes: A case study in <i>Poecilia</i>
Bibliographic record
Abstract
Studies of sex chromosome systems at early stages of divergence are key to understanding the initial process and underlying causes of recombination suppression. However, identifying signatures of divergence in homomorphic sex chromosomes can be challenging due to high levels of sequence similarity between the X and the Y. Variations in methodological precision and underlying data can make all the difference between detecting subtle divergence patterns or missing them entirely. Recent efforts to test for X-Y sequence differentiation in the guppy have led to contradictory results. Here, we apply different analytical methodologies to the same data set to test for the accuracy of different approaches in identifying patterns of sex chromosome divergence in the guppy. Our comparative analysis reveals that the most substantial source of variation in the results of the different analyses lies in the reference genome used. Analyses using custom-made genome assemblies for the focal population or species successfully recover a signal of divergence across different methodological approaches. By contrast, using the distantly related Xiphophorus reference genome results in variable patterns, due to both sequence evolution and structural variations on the sex chromosomes between the guppy and Xiphophorus. Changes in mapping and filtering parameters can additionally introduce noise and obscure the signal. Our results illustrate how analytical differences can alter perceived results and we highlight best practices for the study of nascent sex chromosomes.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".