Next-generation DNA damage sequencing
Bibliographic record
Abstract
Cellular DNA is constantly chemically altered by exogenous and endogenous agents. As all processes of life depend on the transmission of the genetic information, multiple biological processes exist to ensure genome integrity. Chemically damaged DNA has been linked to cancer and aging, therefore it is of great interest to map DNA damage formation and repair to elucidate the distribution of damage on a genome-wide scale. While the low abundance and inability to enzymatically amplify DNA damage are obstacles to genome-wide sequencing, new developments in the last few years have enabled high-resolution mapping of damaged bases. Recently, a number of DNA damage sequencing library construction strategies coupled to new data analysis pipelines allowed the mapping of specific DNA damage formation and repair at high and single nucleotide resolution. Strikingly, these advancements revealed that the distribution of DNA damage is heavily influenced by chromatin states and the binding of transcription factors. In the last seven years, these novel approaches have revealed new genomic maps of DNA damage distribution in a variety of organisms as generated by diverse chemical and physical DNA insults; oxidative stress, chemotherapeutic drugs, environmental pollutants, and sun exposure. Preferred sequences for damage formation and repair have been elucidated, thus making it possible to identify persistent weak spots in the genome as locations predicted to be vulnerable for mutation. As such, sequencing DNA damage will have an immense impact on our ability to elucidate mechanisms of disease initiation, and to evaluate and predict the efficacy of chemotherapeutic drugs.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".