Genome-wide microhomologies enable precise template-free editing of biologically relevant deletion mutations
Bibliographic record
Abstract
The functional effect of a gene edit by designer nucleases depends on the DNA repair outcome at the targeted locus. While non-homologous end joining (NHEJ) repair results in various mutations, microhomology-mediated end joining (MMEJ) creates precise deletions based on the alignment of flanking microhomologies (µHs). Recently, the sequence context surrounding nuclease-induced double strand breaks (DSBs) has been shown to predict repair outcomes, for which µH plays an important role. Here, we survey naturally occurring human deletion variants and identify that 11 million or 57% are flanked by µHs, covering 88% of protein-coding genes. These biologically relevant mutations are candidates for precise creation in a template-free manner by MMEJ repair. Using CRISPR-Cas9 in human induced pluripotent stem cells (hiPSCs), we efficiently create pathogenic deletion mutations for demonstrable disease models with both gain- and loss-of-function phenotypes. We anticipate this dataset and gene editing strategy to enable functional genetic studies and drug screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".