Authors
Hao Yuan, Chunping Song, Huixin Xu, Ying Sun, Christian Anthon, Lars Bolund, Lin Lin, Karim Benabdellah, Ciaran Lee, Yong Hou, Jan Gorodkin, Yonglun Luo
Published in
The CRISPR journal. Mar 27, 2025. Epub Mar 27, 2025.
Abstract
Design of guide RNA (gRNA) with high efficiency and specificity is vital for successful application of the CRISPR gene editing technology. Although many machine learning (ML) and deep learning (DL)-based tools have been developed to predict gRNA activities, a systematic and unbiased evaluation of their predictive performance is still needed. Here, we provide a brief overview of in silico tools for CRISPR design and assess the CRISPR datasets and statistical metrics used for evaluating model performance. We benchmark seven ML and DL-based CRISPR-Cas9 editing efficiency prediction tools across nine CRISPR datasets covering six cell types and three species. The DL models CRISPRon and DeepHF outperform the other models exhibiting greater accuracy and higher Spearman correlation coefficient across multiple datasets. We compile all CRISPR datasets and in silico prediction tools into a GuideNet resource web portal, aiming to facilitate and streamline the sharing of CRISPR datasets. Furthermore, we summarize features affecting CRISPR gene editing activity, providing important insights into model performance and the further development of more accurate CRISPR prediction models.
PMID:
40151952
Bibliographic data and abstract were imported from PubMed on 28 Mar 2025.
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