Revisiting the landscape of evolutionary breakpoints across human genome using multi-way comparison
Bibliographic record
Abstract
Abstract Genome rearrangement is one of the major forces driving the processes of the evolution and disease development. The chromosomal position affected by these rearrangements are called breakpoints. The breakpoints occurring during the evolution of species are known to be non randomly distributed. Detecting their landscape and mapping them to genomic features constitute an important features in both comparative and functional genomics. Several studies have attempted to provide such mapping based on pairwise comparison of genes as conservation anchors. With the availability of more accurate multi-way alignments, we design an approach to identify synteny blocks and evolutionary breakpoints based on UCSC 45-way conservation sequence alignments with 12 selected species. The multi-way designed approach with the mild flexibility of presence of selected species, helped to have a better determination of human lineage-specific evolutionary breakpoints. We identified 261,391 human lineage-specific evolutionary breakpoints across the genome and 2,564 dense regions enriched with biological processes involved in adaptive traits such as response to DNA damage stimulus, cellular response to stress and metabolic process . Moreover, we found 230 regions refractory to evolutionary breakpoints that carry genes associated with crucial developmental process such as organ morphogenesis, skeletal system development, chordate embryonic development, nerve development and regulation of biological process . This initial map of the human genome will help to gain better insight into several studies including developmental studies and cancer rearrangement processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".