On Deriving Synteny Blocks by Compacting Elements
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Bibliographic record
Abstract
Abstract Genomic rearrangements are major drivers of evolution and genetic disease. However, studying rearrangements requires segmenting the genomes of interest into conserved regions, called synteny blocks, that highlight structural differences between genomes. Synteny blocks are typically defined from annotated genes or derived as a by-product of whole-genome alignments. As these procedures are heuristic and do not explicitly model rearrangements, they can obscure real variation, create false similarities, and affect phylogenetic inference. The importance of synteny block definition has long been recognized, as shown for example by discussions on breakpoint reuse, where different definitions of synteny blocks led to different estimates of rearrangement complexity in mammalian genomes. We present a formal framework for deriving synteny blocks directly from sequence data by partitioning genomic elements into blocks that do not contain breakpoints. A breakpoint is defined between a pair of genomes as an adjacency of shared elements that occurs in one genome but not in the other. Synteny blocks are therefore not allowed to span such boundaries, ensuring that rearrangements are not obscured. The framework is fully agnostic to the type of genomic element and applies to any genome representation expressed as sequences of elements, such as non-overlapping alignments, exact matches (MUMs/MEMs), k -mers, unitigs or minimizers. We formalize two optimization problems: minimizing the total genome length after replacement by synteny blocks (the Minimum-Length Synteny Block Problem) and minimizing the number of distinct blocks (the Minimum-Size Synteny Block Problem). We show that both problems are NP-hard in general. However, when blocks are required to be collinear and to contain a shared element, we provide a linear-time algorithm with respect to the number of input elements that simultaneously minimizes both objectives. The resulting method is simple, efficient, and produces large synteny blocks without obscuring rearrangements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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