A comprehensive microsatellite landscape of human Y-DNA at kilobase resolution
Bibliographic record
Abstract
BACKGROUND: Though interest in human simple sequence repeats (SSRs) is increasing, little is known about the exact distributional features of numerous SSRs in human Y-DNA at chromosomal level. Herein, totally 540 maps were established, which could clearly display SSR landscape in every bin of 1 k base pairs (Kbp) along the sequenced part of human reference Y-DNA (NC_000024.10), by our developed differential method for improving the existing method to reveal SSR distributional characteristics in large genomic sequences. RESULTS: The maps show that SSRs accumulate significantly with forming density peaks in at least 2040 bins of 1 Kbp, which involve different coding, noncoding and intergenic regions of the Y-DNA, and 10 especially high density peaks were reported to associate with biological significances, suggesting that the other hundreds of especially high density peaks might also be biologically significant and worth further analyzing. In contrast, the maps also show that SSRs are extremely sparse in at least 207 bins of 1 Kbp, including many noncoding and intergenic regions of the Y-DNA, which is inconsistent with the widely accepted view that SSRs are mostly rich in these regions, and these sparse distributions are possibly due to powerfully regional selection. Additionally, many regions harbor SSR clusters with same or similar motif in the Y-DNA. CONCLUSIONS: These 540 maps may provide the important information of clearly position-related SSR distributional features along the human reference Y-DNA for better understanding the genome structures of the Y-DNA. This study may contribute to further exploring the biological significance and distribution law of the huge numbers of SSRs in human Y-DNA.
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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 | 0.000 |
| 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) | 0.002 | 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".