CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction

William Jobson Pargeter; Rolf Backofen; Van Dinh Tran

doi:10.1007/978-3-031-70368-3_6

CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction

William Jobson Pargeter
, Rolf Backofen
, Van Dinh Tran^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

CRISPR-Cas9 has emerged as a popular gene-editing technique due to its flexibility, precision, and ease of use. It is a complex that consists of a Cas9 protein and a designed, synthetic single guide-RNA (sgRNA) that guides the Cas9 protein to its intended genomic target site, where it induces editing of the DNA through cleavage. Despite its popularity, the potential side effects caused by unintended cleavage of CRISPR-Cas9 have been a critical issue that hinders its development and clinical applications. Therefore, predicting the potential off-target sites will help evaluate the safety of a designed CRISPR-Cas9 system. Many methods have been proposed for off-target site prediction. However, they only obtain moderate results. This is partly due to the high imbalance of data, the choice of network architecture, and the neglect of additional useful information. Here, we introduce CRISPert, a transformer-based model that overcomes these issues. Empirical results from various experimental settings show that our proposed method outperforms many compared methods and confirms its potential for practical use.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings
Editors	Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	92-104
Number of pages	13
ISBN (Print)	9783031703676
DOIs	https://doi.org/10.1007/978-3-031-70368-3_6
State	Published - 2024
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024 - Vilnius, Lithuania Duration: 9 Sep 2024 → 13 Sep 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14947 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
Country/Territory	Lithuania
City	Vilnius
Period	9/09/24 → 13/09/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

CRISPR-Cas
Off-target prediction
Transformer-based model

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-70368-3_6

Cite this

Jobson Pargeter, W., Backofen, R., & Tran, V. D. (2024). CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction. In A. Bifet, J. Davis, T. Krilavičius, M. Kull, E. Ntoutsi, & I. Žliobaitė (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings (pp. 92-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14947 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70368-3_6

Jobson Pargeter, William ; Backofen, Rolf ; Tran, Van Dinh. / CRISPert : A Transformer-Based Model for CRISPR-Cas Off-Target Prediction. Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings. editor / Albert Bifet ; Jesse Davis ; Tomas Krilavičius ; Meelis Kull ; Eirini Ntoutsi ; Indrė Žliobaitė. Springer Science and Business Media Deutschland GmbH, 2024. pp. 92-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Jobson Pargeter, W, Backofen, R & Tran, VD 2024, CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction. in A Bifet, J Davis, T Krilavičius, M Kull, E Ntoutsi & I Žliobaitė (eds), Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14947 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 92-104, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, Vilnius, Lithuania, 9/09/24. https://doi.org/10.1007/978-3-031-70368-3_6

CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction. / Jobson Pargeter, William; Backofen, Rolf; Tran, Van Dinh.
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings. ed. / Albert Bifet; Jesse Davis; Tomas Krilavičius; Meelis Kull; Eirini Ntoutsi; Indrė Žliobaitė. Springer Science and Business Media Deutschland GmbH, 2024. p. 92-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14947 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jobson Pargeter, William

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AU - Tran, Van Dinh

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - CRISPR-Cas9 has emerged as a popular gene-editing technique due to its flexibility, precision, and ease of use. It is a complex that consists of a Cas9 protein and a designed, synthetic single guide-RNA (sgRNA) that guides the Cas9 protein to its intended genomic target site, where it induces editing of the DNA through cleavage. Despite its popularity, the potential side effects caused by unintended cleavage of CRISPR-Cas9 have been a critical issue that hinders its development and clinical applications. Therefore, predicting the potential off-target sites will help evaluate the safety of a designed CRISPR-Cas9 system. Many methods have been proposed for off-target site prediction. However, they only obtain moderate results. This is partly due to the high imbalance of data, the choice of network architecture, and the neglect of additional useful information. Here, we introduce CRISPert, a transformer-based model that overcomes these issues. Empirical results from various experimental settings show that our proposed method outperforms many compared methods and confirms its potential for practical use.

AB - CRISPR-Cas9 has emerged as a popular gene-editing technique due to its flexibility, precision, and ease of use. It is a complex that consists of a Cas9 protein and a designed, synthetic single guide-RNA (sgRNA) that guides the Cas9 protein to its intended genomic target site, where it induces editing of the DNA through cleavage. Despite its popularity, the potential side effects caused by unintended cleavage of CRISPR-Cas9 have been a critical issue that hinders its development and clinical applications. Therefore, predicting the potential off-target sites will help evaluate the safety of a designed CRISPR-Cas9 system. Many methods have been proposed for off-target site prediction. However, they only obtain moderate results. This is partly due to the high imbalance of data, the choice of network architecture, and the neglect of additional useful information. Here, we introduce CRISPert, a transformer-based model that overcomes these issues. Empirical results from various experimental settings show that our proposed method outperforms many compared methods and confirms its potential for practical use.

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KW - Transformer-based model

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Science and Business Media Deutschland GmbH

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Jobson Pargeter W, Backofen R, Tran VD. CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction. In Bifet A, Davis J, Krilavičius T, Kull M, Ntoutsi E, Žliobaitė I, editors, Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 92-104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-70368-3_6