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CRISPR and Genetic Engineering · Journal article
Genome Biology · July 27, 2026
Early or partial results. Treat as a signal, not a conclusion.
inDecay is a parameter-efficient machine-learning system for predicting CRISPR-induced indel frequencies from target sequence context. The tool demonstrates maintained accuracy across somatic cell lines and multiple embryo species (mouse, goat, cattle, porcine), suggesting potential utility in model generation and livestock applications, but lacks comparison to competing methods and prospective validation.
Journal article. Somatic cell lines and embryos from multiple species (mouse, goat, cattle, porcine). Intervention: inDecay prediction system applied to cross-cell-line and cross-species CRISPR editing profiles.
inDecay maintains high accuracy in mouse zygote editing and in goat, cattle and porcine embryos despite being trained primarily on somatic cell lines System is parameter-efficient and cell-type-aware, enabling both in-sample training and out-of-sample fine-tuning
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
If validated prospectively, inDecay could improve precision of preclinical CRISPR editing in mouse models and livestock applications. Current evidence is insufficient to support clinical use or replace existing prediction tools.
A novel computational tool for predicting CRISPR outcomes, demonstrated across multiple cell types and embryo models but lacking clinical validation, prospective efficacy data, or comparison to established methods.
As stated by the source record.
If validated prospectively, inDecay could improve precision of preclinical CRISPR editing in mouse models and livestock applications. Current evidence is insufficient to support clinical use or replace existing prediction tools.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Prediction of CRISPR/Cas outcomes remains unsatisfactory in embryos due to distinct DNA repair preferences and the lack of large-scale embryo editing profiles. We introduce inDecay, a flexible system for predicting the proportion of CRISPR-induced indels from the target sequence. Owing to its parameter-efficient and cell-type-aware design, inDecay performs well in both in-sample training and out-of-sample fine-tuning. Starting with cross-cell-line predictions, we observed that inDecay maintains high accuracy in mouse zygote editing and in goat, cattle and porcine embryos. inDecay may accelerate mouse model generation, livestock embryonic editing, and gene therapy applications.
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