Abstract 1527 Informing models of in-silico DNA digestion
Bibliographic record
Abstract
Many laboratory assays use nucleases to digest DNA. Recently, in-silico simulations of these assays have provided insight into their kinetics and inner workings. These simulations make the convenient assumption that all loci are digested uniformly, but this may not align with the complicated reality of sequence biases in enzymes, and nonuniform chromatin accessibility. We analyzed nuclease activity and targets in the human genome to assess this assumption. First, we identified restriction sites in the human genome and found that some four-cutter enzymes have uniformly distributed targets at the nucleosome scale. Next, we used Hi-C data to determine the relative frequencies with which restriction sites are cleaved by a sequence specific enzyme. We also used DNase seq data to determine the relative frequencies with which loci are cleaved by DNase, a sequence indiscriminate nuclease. We found it was more reasonable to model enzymatic digestion as uniform at rougher resolutions. The results of this work can inform how in-silico simulations model enzymatic DNA digestion. Thanks to Northeastern University's Office of Undergraduate Research and Fellowships, who funded with work under a PEAK award.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".