基于少样本学习的inDecay可准确预测体细胞系与受精卵的CRISPR编辑结果
Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygotes with few-shot learning with inDecay
摘要
该研究针对胚胎中DNA修复偏好不同且缺乏大规模编辑图谱的问题,开发了inDecay系统,可从靶序列预测CRISPR诱导的插入缺失比例。其参数高效且具有细胞类型感知设计,在样本内训练和样本外微调中均表现良好。研究从跨细胞系预测出发,发现inDecay在小鼠受精卵以及山羊、牛和猪胚胎编辑中保持较高准确性。作者认为该方法可加速小鼠模型构建、家畜胚胎编辑和基因治疗应用。
原文摘要
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.
推荐理由:方法新颖,将少样本学习用于跨细胞类型与跨物种的CRISPR编辑结果预测,并验证到小鼠受精卵及多种家畜胚胎,对胚胎基因编辑和动物模型研究有实用价值;但不直接面向临床ART决策。
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