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Peer-reviewedCC BY-NC-NDGenome biology

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

  • Weizhong Zheng1,2
  • Lu Yu1
  • Chao Wu3
  • Shaoxian Cao4
  • Mengying Zhao1
  • Guoliang Wang1
  • Kam Ho So1
  • Jun Song5,6
  • Chen Cheng1,2
  • Joshua W K Ho1,7
  • Xueqin Liu8
  • Meng Wu8
5 more authors
  • Zhonghua Liu5,6
  • Huili Wang9
  • Pentao Liu10,11
  • Guocheng Lan12,13,14
  • Yuanhua Huang15,16,17
17 affiliations
  1. 1School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
  2. 2Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
  3. 3Genome Technologies Limited, Hong Kong Science Park, Hong Kong SAR, China.
  4. 4Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
  5. 5College of Life Science, Northeast Agricultural University, Harbin, 150030, China.
  6. 6Key Laboratory of Animal Cellular and Genetic Engineering of Heilongjiang Province, Northeast Agricultural University, Harbin, China.
  7. 7School of Biomedical Engineering, The University of Hong Kong, Hong Kong SAR, China.
  8. 8Chongqing Academy of Animal Sciences, Chongqing, China.
  9. 9Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing, China. [email protected].
  10. 10School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. [email protected].
  11. 11Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China. [email protected].
  12. 12School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. [email protected].
  13. 13Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China. [email protected].
  14. 14Genome Technologies Limited, Hong Kong Science Park, Hong Kong SAR, China. [email protected].
  15. 15School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. [email protected].
  16. 16Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China. [email protected].
  17. 17Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China. [email protected].
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Abstract

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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Cite

BibTeXRIS
BibTeX
@article{zheng2026accurate,
  title = {{Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygotes with few-shot learning with inDecay.}},
  author = {Weizhong Zheng and Lu Yu and Chao Wu and Shaoxian Cao and Mengying Zhao and Guoliang Wang and Kam Ho So and Jun Song and Chen Cheng and Joshua W K Ho and Xueqin Liu and Meng Wu and Zhonghua Liu and Huili Wang and Pentao Liu and Guocheng Lan and Yuanhua Huang},
  journal = {Genome biology},
  year = {2026},
  month = jul,
  doi = {10.1186/s13059-026-04211-x},
  url = {https://europepmc.org/article/MED/42907902},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygotes with few-shot learning with inDecay.
AU  - Weizhong Zheng
AU  - Lu Yu
AU  - Chao Wu
AU  - Shaoxian Cao
AU  - Mengying Zhao
AU  - Guoliang Wang
AU  - Kam Ho So
AU  - Jun Song
AU  - Chen Cheng
AU  - Joshua W K Ho
AU  - Xueqin Liu
AU  - Meng Wu
AU  - Zhonghua Liu
AU  - Huili Wang
AU  - Pentao Liu
AU  - Guocheng Lan
AU  - Yuanhua Huang
PY  - 2026
DA  - 2026/07/01
JO  - Genome biology
DO  - 10.1186/s13059-026-04211-x
UR  - https://europepmc.org/article/MED/42907902
AB  - 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.
SN  - 1474-7596
SN  - 1474-760X
ER  -