PanScan: A tertiary analysis tool for pangenome graph
Bibliographic record
Abstract
Abstract The genomic representation of populations across the globe is critical to ensuring a comprehensive and equitable human reference. Constructing a pangenome graph reference for different populations is the best approach to addressing local genomic diversities. Although major initiatives across continents are underway to construct pangenome graph references, the field lacks the necessary toolsets for tertiary analysis to characterize telomere-to-telomere (T2T) assemblies and the complexity of haplotypes. PanScan is a bioinformatics software package developed for human pangenome tertiary analysis. It includes multiple modules designed to detect duplicated gene sets from T2T assemblies, identify novel variants and sequences, as well as detect and visualize complex genomic regions through pangenome graph haplotype loops. We have used multiple pangenomes across different populations to assess the tertiary analysis and their accuracy. The tool is designed to streamline tertiary analysis and is compatible with multiple pangenome graph construction algorithms. PanScan is freely available on GitHub ( https://github.com/CATG-Github/panscan ), where users can provide human pangenome assemblies or VCF files as inputs for automated analyses through command-line operations on Linux systems. Graphical Abstract
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".