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SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a...

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Detalles Bibliográficos
Autores principales: Kim, Hui Kwon, Kim, Younggwang, Lee, Sungtae, Min, Seonwoo, Bae, Jung Yoon, Choi, Jae Woo, Park, Jinman, Jung, Dongmin, Yoon, Sungroh, Kim, Hyongbum Henry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834390/
https://www.ncbi.nlm.nih.gov/pubmed/31723604
http://dx.doi.org/10.1126/sciadv.aax9249
Descripción
Sumario:We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.