SexFindR: A computational workflow to identify young and old sex chromosomes
Bibliographic record
Abstract
Abstract Sex chromosomes have evolved frequently across the tree of life, and have been a source of fascination for decades due to their unique evolutionary trajectories. They are hypothesised to be important drivers in a broad spectrum of biological processes and are the focus of a rich body of evolutionary theory. Whole-genome sequencing provides exciting opportunities to test these theories through contrasts between independently evolved sex chromosomes across the full spectrum of their evolutionary lifecycles. However, identifying sex chromosomes, particularly nascent ones, is challenging, often requiring specific combinations of methodologies. This is a major barrier to progress in the field and can result in discrepancies between studies that apply different approaches. Currently, no single pipeline exists to integrate data across these methods in a statistical framework to identify sex chromosomes at all ages and levels of sequence divergence. To address this, we present SexFindR, a comprehensive workflow to improve robustness and transparency in identifying sex-linked sequences. We validate our approach using publicly available data from five species that span the continuum of sex chromosome divergence, from homomorphic sex chromosomes with only a single SNP that determines sex, to heteromorphic sex chromosomes with extensive degeneration. Next, we apply SexFindR to our large-scale population genomics dataset for sea lamprey, a jawless vertebrate whose sex determination system remains a mystery despite decades of research. We decisively show that sea lamprey do not harbour sex-linked sequences in their somatic genome, leaving open the possibility that sex is determined environmentally or within the germline genome.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".