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Prediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature.

TitlePrediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature.
Publication TypeJournal Article
Year of Publication2019
AuthorsLiu Q, He D, Xie L
JournalPLoS Comput Biol
Volume15
Issue10
Paginatione1007480
Date Published2019 10
ISSN1553-7358
KeywordsAlgorithms, Cell Line, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems, Databases, Genetic, Deep Learning, Forecasting, Gene Editing, Gene Regulatory Networks, Genome, Humans, RNA Editing, RNA, Guide, Substrate Specificity
Abstract

CRISPR-Cas is a powerful genome editing technology and has a great potential for in vivo gene therapy. Successful translational application of CRISPR-Cas to biomedicine still faces many safety concerns, including off-target side effect, cell fitness problem after CRISPR-Cas treatment, and on-target genome editing side effect in undesired tissues. To solve these issues, it is needed to design sgRNA with high cell-specific efficacy and specificity. Existing single-guide RNA (sgRNA) design tools mainly depend on a sgRNA sequence and the local information of the targeted genome, thus are not sufficient to account for the difference in the cellular response of the same gene in different cell types. To incorporate cell-specific information into the sgRNA design, we develop novel interpretable machine learning models, which integrate features learned from advanced transformer-based deep neural network with cell-specific gene property derived from biological network and gene expression profile, for the prediction of CRISPR-Cas9 and CRISPR-Cas12a efficacy and specificity. In benchmark studies, our models significantly outperform state-of-the-art algorithms. Furthermore, we find that the network-based gene property is critical for the prediction of cell-specific post-treatment cellular response. Our results suggest that the design of efficient and safe CRISPR-Cas needs to consider cell-specific information of genes. Our findings may bolster developing more accurate predictive models of CRISPR-Cas across a broad spectrum of biological conditions as well as provide new insight into developing efficient and safe CRISPR-based gene therapy.

DOI10.1371/journal.pcbi.1007480
Alternate JournalPLoS Comput. Biol.
PubMed ID31658261
PubMed Central IDPMC6837542
Grant ListR01 AG057555 / AG / NIA NIH HHS / United States
R01 GM122845 / GM / NIGMS NIH HHS / United States
R01 LM011986 / LM / NLM NIH HHS / United States