Prime-editing efficiency (PRIDICT2.0)
prime_editing_efficiencyPredict per-pegRNA prime-editing efficiency for one edit with PRIDICT2.0, and return the top-scoring pegRNA designs ranked by it. Takes the target as context, the edit in brackets, then context — ACGT...(A/G)...ACGT, with roughly 100+ bp each side — and enumerates PBS/RTT length combinations, scoring every one in HEK293 and K562. Each candidate comes back with both scores, its percentile against the training library, its rank, the spacer, PBS and RTT lengths, the full pegRNA, and Golden Gate cloning oligos. Use it to CHOOSE between designs; the number is not a promised editing percentage. PREDICTED, NOT MEASURED (Spearman ρ = 0.85 on held-out data from the libraries it was trained on). Spearman rho of about 0.85 for intended edits on held-out library data — the best-validated figure of any model in this registry, and roughly double OSTIR's 0.39 on independent data. That figure is still within the library and cell lines it was trained on. Valid for: human sequence, and efficiency ranking within one locus. It is parameterised on HEK293 and K562; your cell type, delivery method, and chromatin context will all move the absolute efficiency, chromatin alone by severalfold. Nothing here is predicted for a non-human host or for editors outside the PE2/PE3 architecture the training libraries used.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| topN | No | How many top-ranked pegRNAs to return, out of the hundreds enumerated. Max 50. | |
| cellType | No | Which trained context to RANK by. Both scores are always returned; this decides the ordering. There is no generic-mammalian option because the model has no such training data. | HEK |
| sequence | Yes | Target with the edit in brackets: context, then (original/edited), then context. Roughly 100+ bp each side — the model reads that context. Keep unchanged flanking bases OUTSIDE the brackets: T(a/g)C, not (TAC/TGC). Insertions and deletions leave one side empty, e.g. (/AGG) or (AGG/). | |
| use5Folds | No | Average all five trained folds instead of the first. Modestly steadier scores for five times the compute, and it is charged five times as much. |