Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygotes with few-shot learning with inDecay

2026-07-27

Weizhong Zheng

Lu Yu

吴

吴超 (Chao Wu)

Shaoxian Cao

Mengying Zhao

Guoliang Wang

Kam Ho So

Jun Song

Cheng Chen

Joshua W. K. Ho

Xueqin Liu

Meng Wu

Zhonghua Liu

Huili Wang *

Pentao Liu *

Guocheng Lan *

Yuanhua Huang *

DOI：10.1186/s13059-026-04211-x

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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.

Keywords:

CRISPR editing

DNA repair

Machine learning

Editing outcome prediction

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygotes with few-shot learning with inDecay

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