Segpy: a streamlined, user-friendly pipeline for variant segregation analysis
Bibliographic record
Abstract
Abstract Understanding the role of genetic variants in disease is essential for diagnostics and the advancement of genomic medicine. While the advent of high-throughput sequencing has been matched by the development of sophisticated genomic analysis tools, these packages often involve complex analytical procedures that can be challenging for researchers with limited computational experience. Additionally, modern genomic datasets require high-performance computing (HPC) systems, which may be difficult to implement for unfamiliar users. To address these challenges, we introduce Segpy, a streamlined, user-friendly pipeline for variant segregation analysis that integrates seamlessly with HPC environments. Segpy supports single-family, multi-family, and population-based datasets, allowing researchers to evaluate how genetic variants co-segregate with disease in pedigree-based analyses and compare allele frequencies between affected and unaffected individuals in case-control analyses. To date, the application of Segpy has facilitated the identification of genetic variants contributing to many human diseases and is now available as a publicly available framework.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.035 |
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".