CRISPR-DIPOFF: An Interpretable Deep Learning Approach for CRISPR Cas-9 Off-Target Prediction

Authors

Md Toufikuzzaman
Md Abul Hassan Samee
M Sohel Rahman

Publication Date

1-22-2024

Journal

Briefings in Bioinformatics

DOI

10.1093/bib/bbad530

PMID

38388680

PMCID

PMC10883906

PubMedCentral® Posted Date

2-8-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

CRISPR-Cas Systems, Deep Learning, Gene Editing, RNA, Guide, CRISPR-Cas Systems, Neural Networks, Computer, CRISPR Cas-9, Off-Target, LSTM, Interpretation, Genetic Algorithm, Transformers

Abstract

CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.

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