Analyse de données et modèle pour l'étude de la chromatine, des G-quadruplexes et de la réparation de l'ADN
Bibliographic record
Abstract
DNA Double-strand breaks (DSBs) are harmful lesions that can occur on the genome following exposure to genotoxic agents but also due to endogenous causes, among which the formation of DNA secondary structures, such as G-quadruplexes (G4). Previous methods were developed to computationaly predict G4s based on specific motifs, and recent Next Generation Sequencing approaches identified G4 distribution genome-wide. I developed a novel Deep learning model to predict active G4 regions using the DNA sequences and chromatin accessibility. Using this model, we found new motifs predictors including known transcription factors that could regulate directly or indirectly G4s activity. We also mapped thousand of active G4s regions that can be used in cancer therapy to identify potential targets of recent G4-ligand drugs. Moreover, once induced on the genome, DSBs trigger local chromatin modifications including the phosphorylation of the H2AX histone variant (gamma H2AX) by the ATM kinase, to form megabase-sized repair foci. How these domains are formed to enable rapid signaling of DSBs, and how these local chromatin changes are handled by the cell is still unclear. We found, that the recruitment of repair components and the phosphorylation of H2AX is governed by pre-existing Topologically Associating Domain (TADs). Moreover we discovered that an unidirectional loop-extrusion process mediated by the cohesin complex takes place on both side of the DSBs, which allow repair foci formation by ATM. We also found, at a global scale, that DSBs can form a novel "D" chromatin compartment, composed of gH2AX-decorated chromatin domains, but also of DNA damage responsive genes, suggesting a role of DSB clustering in activating the DNA Damage Response.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".