Efficient identification of Y chromosome sequences in the human and <i>Drosophila</i> genomes
Bibliographic record
Abstract
Notwithstanding their biological importance, Y chromosomes remain poorly known in most species. A major obstacle to their study is the identification of Y chromosome sequences; due to its high content of repetitive DNA, in most genome projects, the Y chromosome sequence is fragmented into a large number of small, unmapped scaffolds. Identification of Y-linked genes among these fragments has yielded important insights about the origin and evolution of Y chromosomes, but the process is labor intensive, restricting studies to a small number of species. Apart from these fragmentary assemblies, in a few mammalian species, the euchromatic sequence of the Y is essentially complete, owing to painstaking BAC mapping and sequencing. Here we use female short-read sequencing and k-mer comparison to identify Y-linked sequences in two very different genomes, Drosophila virilis and human. Using this method, essentially all D. virilis scaffolds were unambiguously classified as Y-linked or not Y-linked. We found 800 new scaffolds (totaling 8.5 Mbp), and four new genes in the Y chromosome of D. virilis, including JYalpha, a gene involved in hybrid male sterility. Our results also strongly support the preponderance of gene gains over gene losses in the evolution of the Drosophila Y. In the intensively studied human genome, used here as a positive control, we recovered all previously known genes or gene families, plus a small amount (283 kb) of new, unfinished sequence. Hence, this method works in large and complex genomes and can be applied to any species with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".