DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes

Zhang, Guishan; Xie, Huanzeng; Dai, Xianhua

doi:10.3390/ijms252010928

Open AccessArticle

DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes

by

Guishan Zhang

¹

,

Huanzeng Xie

¹ and

Xianhua Dai

^2,*

¹

College of Engineering, Shantou University, Shantou 515063, China

²

School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China

^*

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2024, 25(20), 10928; https://doi.org/10.3390/ijms252010928

Submission received: 27 August 2024 / Revised: 1 October 2024 / Accepted: 8 October 2024 / Published: 11 October 2024

(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine: 2nd Edition)

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Abstract

CRISPR/Cas9 has been applied to edit the genome of various organisms, but our understanding of editing outcomes at specific sites after Cas9-mediated DNA cleavage is still limited. Several deep learning-based methods have been proposed for repair outcome prediction; however, there is still room for improvement in terms of performance regarding frameshifts and model interpretability. Here, we present DeepIndel, an end-to-end multi-label regression model for predicting repair outcomes based on the BERT-base module. We demonstrate that our model outperforms existing methods in terms of accuracy and generalizability across various metrics. Furthermore, we utilized Deep SHAP to visualize the importance of nucleotides at various positions for DNA sequence and found that mononucleotides and trinucleotides in DNA sequences surrounding the cut site play a significant role in repair outcome prediction.

Keywords: CRISPR/Cas9; BERT; repair outcomes; Deep SHAP

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MDPI and ACS Style

Zhang, G.; Xie, H.; Dai, X. DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes. Int. J. Mol. Sci. 2024, 25, 10928. https://doi.org/10.3390/ijms252010928

AMA Style

Zhang G, Xie H, Dai X. DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes. International Journal of Molecular Sciences. 2024; 25(20):10928. https://doi.org/10.3390/ijms252010928

Chicago/Turabian Style

Zhang, Guishan, Huanzeng Xie, and Xianhua Dai. 2024. "DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes" International Journal of Molecular Sciences 25, no. 20: 10928. https://doi.org/10.3390/ijms252010928

APA Style

Zhang, G., Xie, H., & Dai, X. (2024). DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes. International Journal of Molecular Sciences, 25(20), 10928. https://doi.org/10.3390/ijms252010928

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes

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