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Accounting for small variations in the tracrRNA sequence improves sgRNA activity predictions for CRISPR screening

Author

Listed:
  • Peter C. DeWeirdt

    (Broad Institute of MIT and Harvard)

  • Abby V. McGee

    (Broad Institute of MIT and Harvard)

  • Fengyi Zheng

    (Broad Institute of MIT and Harvard)

  • Ifunanya Nwolah

    (Broad Institute of MIT and Harvard
    Arbor Biotechnologies)

  • Mudra Hegde

    (Broad Institute of MIT and Harvard
    Thermo Fisher Scientific)

  • John G. Doench

    (Broad Institute of MIT and Harvard)

Abstract

CRISPR technology is a powerful tool for studying genome function. To aid in picking sgRNAs that have maximal efficacy against a target of interest from many possible options, several groups have developed models that predict sgRNA on-target activity. Although multiple tracrRNA variants are commonly used for screening, no existing models account for this feature when nominating sgRNAs. Here we develop an on-target model, Rule Set 3, that makes optimal predictions for multiple tracrRNA variants. We validate Rule Set 3 on a new dataset of sgRNAs tiling essential and non-essential genes, demonstrating substantial improvement over prior prediction models. By analyzing the differences in sgRNA activity between tracrRNA variants, we show that Pol III transcription termination is a strong determinant of sgRNA activity. We expect these results to improve the performance of CRISPR screening and inform future research on tracrRNA engineering and sgRNA modeling.

Suggested Citation

  • Peter C. DeWeirdt & Abby V. McGee & Fengyi Zheng & Ifunanya Nwolah & Mudra Hegde & John G. Doench, 2022. "Accounting for small variations in the tracrRNA sequence improves sgRNA activity predictions for CRISPR screening," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33024-2
    DOI: 10.1038/s41467-022-33024-2
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    Cited by:

    1. Luke Hoberecht & Pirunthan Perampalam & Aaron Lun & Jean-Philippe Fortin, 2022. "A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies," Nature Communications, Nature, vol. 13(1), pages 1-20, December.

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