SuperDecode: An integrated toolkit for analyzing mutations induced by genome editing
Bibliographic record
Abstract
Genome editing using CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein) or other systems has become a cornerstone of numerous biological and applied research fields. However, detecting the resulting mutations by analyzing sequencing data remains time consuming and inefficient. In response to this issue, we designed SuperDecode, an integrated software toolkit for analyzing editing outcomes using a range of sequencing strategies. SuperDecode comprises three modules, DSDecodeMS, HiDecode, and LaDecode, each designed to automatically decode mutations from Sanger, high-throughput short-read, and long-read sequencing data, respectively, from targeted PCR amplicons. By leveraging specific strategies for constructing sequencing libraries of pooled multiple amplicons, HiDecode and LaDecode facilitate large-scale identification of mutations induced by single or multiplex target-site editing in a cost-effective manner. We demonstrate the efficacy of SuperDecode by analyzing mutations produced using different genome editing tools (CRISPR/Cas, base editing, and prime editing) in different materials (diploid and tetraploid rice and protoplasts), underscoring its versatility in decoding genome editing outcomes across different applications. Furthermore, this toolkit can be used to analyze other genetic variations, as exemplified by its ability to estimate the C-to-U editing rate of the cellular RNA of a mitochondrial gene. SuperDecode offers both a standalone software package and a web-based version, ensuring its easy access and broad compatibility across diverse computer systems. Thus, SuperDecode provides a comprehensive platform for analyzing a wide array of mutations, advancing the utility of genome editing for scientific research and genetic engineering.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".