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Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning

Nature Biotechnology volume 42, pages 628–637 (2024)Cite this article

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Abstract

Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically designed mismatches and insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G–U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling the use of RNA-targeting CRISPRs to precisely control gene dosage.

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Fig. 1: Pooled CRISPR–Cas13 essentiality screen assaying Cas13d gRNA efficacy.
Fig. 2: Large-scale mapping of Cas13d gRNA mismatch activity.
Fig. 3: A deep learning model to predict optimal Cas13d gRNAs.
Fig. 4: Training TIGER using gRNAs with mismatches enables prediction of off-target activity and transcript modulation using gRNAs with SMs.

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Data availability

All data generated in this study have been deposited at NCBI Gene Expression Omnibus (GEO) with the accession number GSE232228. Flow cytometry screen data from ref. 7 is available under the accession number GSE142675.

Code availability

Code to run Cas13d on-target and off-target TIGER models has been deposited on Github (https://github.com/daklab/tiger). A web-accessible version of TIGER is available at https://tiger.nygenome.org/.

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Acknowledgements

We thank the entire Sanjana and Knowles Labs for their support and advice. D.A.K. is supported by Columbia and NYGC startup funds, NIH/NCI (R21CA272345) and an NSF CAREER (DBI2146398). N.E.S. is supported by NYU and NYGC startup funds, NIH/NHGRI (DP2HG010099), NIH/NCI (R01CA218668), NIH/NIGMS (R01GM138635), DARPA (D18AP00053), Cancer Research Institute and the Simons Foundation for Autism Research Initiative.

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  1. These authors contributed equally: Hans-Hermann Wessels, Andrew Stirn.

Authors and Affiliations

  1. New York Genome Center, New York City, NY, USA

    Hans-Hermann Wessels, Andrew Stirn, Alejandro Méndez-Mancilla, Sydney K. Hart, David A. Knowles & Neville E. Sanjana

  2. Department of Biology, New York University, New York City, NY, USA

    Hans-Hermann Wessels, Alejandro Méndez-Mancilla, Sydney K. Hart & Neville E. Sanjana

  3. Department of Computer Science, Columbia University, New York City, NY, USA

    Andrew Stirn, Eric J. Kim & David A. Knowles

  4. Data Science Institute, Columbia University, New York City, NY, USA

    David A. Knowles

  5. Department of Systems Biology, Columbia University, New York City, NY, USA

    David A. Knowles

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Contributions

H.W. and N.E.S. conceived the study. H.W., A.S., D.A.K. and N.E.S. designed the experiments. H.W. and A.M. cloned libraries and performed the CRISPR screens. S.K.H. assisted with cell culture for pooled screens. H.W., A.S., D.A.K. and N.E.S. analyzed the data and developed the deep learning model. A.S. and E.J.K. implemented the web-based online TIGER tool. H.W., A.S., D.A.K. and N.E.S. wrote the paper with input from all authors.

Corresponding authors

Correspondence to David A. Knowles or Neville E. Sanjana.

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Competing interests

The New York Genome Center and New York University have applied for patents relating to the work in this article. H.W. is a cofounder of Neptune Biotech. N.E.S. is an advisor to Qiagen and is a cofounder of OverT Bio. The other authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Supplementary Note.

Reporting Summary

Supplementary Data 1

Off-target screen target genes.

Supplementary Data 2

Off-target screen gRNA annotation.

Supplementary Data 3

Off-target screen raw counts.

Supplementary Data 4

Off-target screen gRNA depletion.

Supplementary Data 5

TIGER on-target screen gRNA annotation.

Supplementary Data 6

TIGER on-target screen raw counts.

Supplementary Data 7

TIGER on-target gRNA depletion.

Supplementary Data 8

TIGER on-target gene depletion.

Supplementary Data 9

Off-target screen relative SM gRNA activities.

Supplementary Data 10

TIGER titration screen gRNA annotation.

Supplementary Data 11

TIGER titration screen raw gRNA counts.

Supplementary Data 12

TIGER titration screen gRNA depletion.

Supplementary Data 13

TIGER titration screen relative SM gRNA activities.

Supplementary Data 14

Oligonucleotides used in this study.

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Wessels, HH., Stirn, A., Méndez-Mancilla, A. et al. Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning. Nat Biotechnol 42, 628–637 (2024). https://doi.org/10.1038/s41587-023-01830-8

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