Fast characterization of segmental duplication structure in multiple genome assemblies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MOTIVATION: The increasing availability of high-quality genome assemblies raised interest in the characterization of genomic architecture. Major architectural elements, such as common repeats and segmental duplications (SDs), increase genome plasticity that stimulates further evolution by changing the genomic structure and inventing new genes. Optimal computation of SDs within a genome requires quadratic-time local alignment algorithms that are impractical due to the size of most genomes. Additionally, to perform evolutionary analysis, one needs to characterize SDs in multiple genomes and find relations between those SDs and unique (non-duplicated) segments in other genomes. A naïve approach consisting of multiple sequence alignment would make the optimal solution to this problem even more impractical. Thus there is a need for fast and accurate algorithms to characterize SD structure in multiple genome assemblies to better understand the evolutionary forces that shaped the genomes of today. RESULTS: Here we introduce a new approach, BISER, to quickly detect SDs in multiple genomes and identify elementary SDs and core duplicons that drive the formation of such SDs. BISER improves earlier tools by (i) scaling the detection of SDs with low homology to multiple genomes while introducing further 7-33[Formula: see text] speed-ups over the existing tools, and by (ii) characterizing elementary SDs and detecting core duplicons to help trace the evolutionary history of duplications to as far as 300 million years. AVAILABILITY AND IMPLEMENTATION: BISER is implemented in Seq programming language and is publicly available at https://github.com/0xTCG/biser .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it