Modeling CRISPR-Cas13d On-target and Off-target Effects Using Machine Learning Approaches

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Feb 10

PMID 36765063

Authors

Xiaolong Cheng

Zexu Li

Ruocheng Shan

Zihan Li

Shengnan Wang

Wenchang Zhao

Han Zhang

Lumen Chao

Jian Peng

Teng Fei

Wei Li

Affiliations

Soon will be listed here.

Abstract

A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org .

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References

Lorenz R, Bernhart S, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler P . ViennaRNA Package 2.0. Algorithms Mol Biol. 2011; 6:26. PMC: 3319429. DOI: 10.1186/1748-7188-6-26. View

Ackerman C, Myhrvold C, Thakku S, Freije C, Metsky H, Yang D . Massively multiplexed nucleic acid detection with Cas13. Nature. 2020; 582(7811):277-282. PMC: 7332423. DOI: 10.1038/s41586-020-2279-8. View

Doench J, Fusi N, Sullender M, Hegde M, Vaimberg E, Donovan K . Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016; 34(2):184-191. PMC: 4744125. DOI: 10.1038/nbt.3437. View

Aguirre A, Meyers R, Weir B, Vazquez F, Zhang C, Ben-David U . Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. Cancer Discov. 2016; 6(8):914-29. PMC: 4972686. DOI: 10.1158/2159-8290.CD-16-0154. View

Pallaseni A, Peets E, Koeppel J, Weller J, Vanderstichele T, Ho U . Predicting base editing outcomes using position-specific sequence determinants. Nucleic Acids Res. 2022; 50(6):3551-3564. PMC: 8989541. DOI: 10.1093/nar/gkac161. View

Kim H, Min S, Song M, Jung S, Choi J, Kim Y . Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat Biotechnol. 2018; 36(3):239-241. DOI: 10.1038/nbt.4061. View

Ghandi M, Huang F, Jane-Valbuena J, Kryukov G, Lo C, McDonald 3rd E . Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569(7757):503-508. PMC: 6697103. DOI: 10.1038/s41586-019-1186-3. View

Kim H, Kim Y, Lee S, Min S, Bae J, Choi J . SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Sci Adv. 2019; 5(11):eaax9249. PMC: 6834390. DOI: 10.1126/sciadv.aax9249. View

Li J, Chen H, Wang Y, Chen M, Liang H . Next-Generation Analytics for Omics Data. Cancer Cell. 2020; 39(1):3-6. PMC: 8336660. DOI: 10.1016/j.ccell.2020.09.002. View

10.

Taliaferro J, Lambert N, Sudmant P, Dominguez D, Merkin J, Alexis M . RNA Sequence Context Effects Measured In Vitro Predict In Vivo Protein Binding and Regulation. Mol Cell. 2016; 64(2):294-306. PMC: 5107313. DOI: 10.1016/j.molcel.2016.08.035. View

11.

Crosetto N, Mitra A, Silva M, Bienko M, Dojer N, Wang Q . Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing. Nat Methods. 2013; 10(4):361-5. PMC: 3651036. DOI: 10.1038/nmeth.2408. View

12.

Shinoda H, Taguchi Y, Nakagawa R, Makino A, Okazaki S, Nakano M . Amplification-free RNA detection with CRISPR-Cas13. Commun Biol. 2021; 4(1):476. PMC: 8055673. DOI: 10.1038/s42003-021-02001-8. View

13.

Wei J, Lotfy P, Faizi K, Baungaard S, Gibson E, Wang E . Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting. Cell Syst. 2023; 14(12):1087-1102.e13. DOI: 10.1016/j.cels.2023.11.006. View

14.

Xu H, Xiao T, Chen C, Li W, Meyer C, Wu Q . Sequence determinants of improved CRISPR sgRNA design. Genome Res. 2015; 25(8):1147-57. PMC: 4509999. DOI: 10.1101/gr.191452.115. View

15.

East-Seletsky A, OConnell M, Burstein D, Knott G, Doudna J . RNA Targeting by Functionally Orthogonal Type VI-A CRISPR-Cas Enzymes. Mol Cell. 2017; 66(3):373-383.e3. PMC: 5999320. DOI: 10.1016/j.molcel.2017.04.008. View

16.

Tsai S, Zheng Z, Nguyen N, Liebers M, Topkar V, Thapar V . GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotechnol. 2014; 33(2):187-197. PMC: 4320685. DOI: 10.1038/nbt.3117. View

17.

Zhang Y, Nguyen T, Zhang X, Wang L, Phan T, Clohessy J . Optimized RNA-targeting CRISPR/Cas13d technology outperforms shRNA in identifying functional circRNAs. Genome Biol. 2021; 22(1):41. PMC: 7818937. DOI: 10.1186/s13059-021-02263-9. View

18.

Abudayyeh O, Gootenberg J, Konermann S, Joung J, Slaymaker I, Cox D . C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science. 2016; 353(6299):aaf5573. PMC: 5127784. DOI: 10.1126/science.aaf5573. View

19.

Morgens D, Wainberg M, Boyle E, Ursu O, Araya C, Tsui C . Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat Commun. 2017; 8:15178. PMC: 5424143. DOI: 10.1038/ncomms15178. View

20.

Ding F, Lai J, Gao Y, Wang G, Shang J, Zhang D . NEAT1/miR-23a-3p/KLF3: a novel regulatory axis in melanoma cancer progression. Cancer Cell Int. 2019; 19:217. PMC: 6706883. DOI: 10.1186/s12935-019-0927-6. View