Faculty Opinions recommendation of CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions.
Bibliographic record
Abstract
Results of PARP inhibitor CRISPR screens, source data for mouse xenograft experiments, unprocessed images of immunoblots and examples of gating strategies for FACS experiments are provided as Supplementary Information.All other datasets generated during this study are available from the corresponding authors upon reasonable request.Author contributions MZ performed the initial CRISPR screens with the help of MA, AM, MC, SA and JM; TH analyzed the data.MZ and OM performed suppressor screens; AM helped with data analysis.Unless otherwise stated, MZ and OM, with input from MAMR, performed all additional experiments and data analysis.MAMR performed biochemical characterization of RER-deficient RNase H2, and together with ŽT and AF contributed to the generation of HeLa and HCT116 RNASEH2A-KO cell lines.AA, under the supervision of TS, conducted ex-vivo CLL studies and CGH arrays.SP and PM clinically characterized CLL patients and provided CLL blood samples.RC performed MLPA assays.WY, MC and ML, under the supervision of JB, analysed CNA in the RB1-RNASEH2B region in CRPCs.MM and OM, under the supervision of VGB, conducted xenograft experiments.APJ and DD designed and directed the study.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.067 |
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".