Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and
Chromatin Accessibility Data
Azim Dehghani Amirabad Yanfei Zhang Artem Moskalev Sowmya Rajesh Tommaso Mansi Shuwei Li Mangal Prakash Rui Liao
作者信息
Abstract
Predicting guide RNA (gRNA) activity is critical for effective CRISPR-Cas12
genome editing but remains challenging due to limited data, variation across
protospacer adjacent motifs (PAMs-short sequence requirements for Cas binding),
and reliance on large-scale training. We investigate whether pre-trained
biological foundation model originally trained on transcriptomic data can
improve gRNA activity estimation even without domain-specific pre-training.
Using embeddings from existing RNA foundation model as input to lightweight
regressor, we show substantial gains over traditional baselines. We also
integrate chromatin accessibility data to capture regulatory context, improving
performance further. Our results highlight the effectiveness of pre-trained
foundation models and chromatin accessibility data for gRNA activity
prediction.引用本文复制引用
Azim Dehghani Amirabad,Yanfei Zhang,Artem Moskalev,Sowmya Rajesh,Tommaso Mansi,Shuwei Li,Mangal Prakash,Rui Liao.Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and
Chromatin Accessibility Data[EB/OL].(2025-06-12)[2025-12-23].https://arxiv.org/abs/2506.11182.学科分类
遗传学/生物科学研究方法、生物科学研究技术
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