Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. / Xiang, Xi; Corsi, Giulia I.; Anthon, Christian; Qu, Kunli; Pan, Xiaoguang; Liang, Xue; Han, Peng; Dong, Zhanying; Liu, Lijun; Zhong, Jiayan; Ma, Tao; Wang, Jinbao; Zhang, Xiuqing; Jiang, Hui; Xu, Fengping; Liu, Xin; Xu, Xun; Wang, Jian; Yang, Huanming; Bolund, Lars; Church, George M.; Lin, Lin; Gorodkin, Jan; Luo, Yonglun.

I: Nature Communications, Bind 12, Nr. 1, 3238, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Xiang, X, Corsi, GI, Anthon, C, Qu, K, Pan, X, Liang, X, Han, P, Dong, Z, Liu, L, Zhong, J, Ma, T, Wang, J, Zhang, X, Jiang, H, Xu, F, Liu, X, Xu, X, Wang, J, Yang, H, Bolund, L, Church, GM, Lin, L, Gorodkin, J & Luo, Y 2021, 'Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning', Nature Communications, bind 12, nr. 1, 3238. https://doi.org/10.1038/s41467-021-23576-0

APA

Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., Ma, T., Wang, J., Zhang, X., Jiang, H., Xu, F., Liu, X., Xu, X., Wang, J., Yang, H., ... Luo, Y. (2021). Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. Nature Communications, 12(1), [3238]. https://doi.org/10.1038/s41467-021-23576-0

Vancouver

Xiang X, Corsi GI, Anthon C, Qu K, Pan X, Liang X o.a. Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. Nature Communications. 2021;12(1). 3238. https://doi.org/10.1038/s41467-021-23576-0

Author

Xiang, Xi ; Corsi, Giulia I. ; Anthon, Christian ; Qu, Kunli ; Pan, Xiaoguang ; Liang, Xue ; Han, Peng ; Dong, Zhanying ; Liu, Lijun ; Zhong, Jiayan ; Ma, Tao ; Wang, Jinbao ; Zhang, Xiuqing ; Jiang, Hui ; Xu, Fengping ; Liu, Xin ; Xu, Xun ; Wang, Jian ; Yang, Huanming ; Bolund, Lars ; Church, George M. ; Lin, Lin ; Gorodkin, Jan ; Luo, Yonglun. / Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. I: Nature Communications. 2021 ; Bind 12, Nr. 1.

Bibtex

@article{1c0b885e023d47d592e51b1a97b2746b,
title = "Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning",
abstract = "The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.",
author = "Xi Xiang and Corsi, {Giulia I.} and Christian Anthon and Kunli Qu and Xiaoguang Pan and Xue Liang and Peng Han and Zhanying Dong and Lijun Liu and Jiayan Zhong and Tao Ma and Jinbao Wang and Xiuqing Zhang and Hui Jiang and Fengping Xu and Xin Liu and Xun Xu and Jian Wang and Huanming Yang and Lars Bolund and Church, {George M.} and Lin Lin and Jan Gorodkin and Yonglun Luo",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
doi = "10.1038/s41467-021-23576-0",
language = "English",
volume = "12",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

AU - Xiang, Xi

AU - Corsi, Giulia I.

AU - Anthon, Christian

AU - Qu, Kunli

AU - Pan, Xiaoguang

AU - Liang, Xue

AU - Han, Peng

AU - Dong, Zhanying

AU - Liu, Lijun

AU - Zhong, Jiayan

AU - Ma, Tao

AU - Wang, Jinbao

AU - Zhang, Xiuqing

AU - Jiang, Hui

AU - Xu, Fengping

AU - Liu, Xin

AU - Xu, Xun

AU - Wang, Jian

AU - Yang, Huanming

AU - Bolund, Lars

AU - Church, George M.

AU - Lin, Lin

AU - Gorodkin, Jan

AU - Luo, Yonglun

N1 - Publisher Copyright: © 2021, The Author(s).

PY - 2021

Y1 - 2021

N2 - The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.

AB - The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.

U2 - 10.1038/s41467-021-23576-0

DO - 10.1038/s41467-021-23576-0

M3 - Journal article

C2 - 34050182

AN - SCOPUS:85107009021

VL - 12

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 3238

ER -

ID: 272115399