5.9
CiteScore
5.9
Impact Factor
Volume 49 Issue 3
Mar.  2022
Turn off MathJax
Article Contents

Sequence motifs and prediction model of GBE editing outcomes based on target library analysis and machine learning

doi: 10.1016/j.jgg.2021.11.007
Funds:

This work was financially supported by the National Key Research and Development Program of China (2018YFA0903700), the National Natural Science Foundation of China (31861143019, 31770105, 32001041), a Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project (TSBICIP-KJGG-001) and Tianjin Natural Science Foundation (20JCYBJC00310).

  • Received Date: 2021-07-02
  • Accepted Date: 2021-11-14
  • Rev Recd Date: 2021-11-08
  • Publish Date: 2021-12-02
  • loading
  • Arbab, M., Shen, M.W., Mok, B., Wilson, C., Matuszek, Z., Cassa, C.A.,Liu, D.R., 2020. Determinants of base editing outcomes from target library analysis and machine learning. Cell 182, 463-480. e430
    Chen, L.W., Park, J.E., Paa, P., Rajakumar, P.D., Prekop, H.T., Chew, Y.T., Manivannan, S.N.,Chew, W.L., 2021. Programmable C:G to G:C genome editing with CRISPR-Cas9-directed base excision repair proteins. Nat. Commun. 12, 1384
    Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., et l., 2018. DeepCRISPR:optimized CRISPR guide RNA design by deep learning. Genome Biol. 19, 80
    Cuperus, J.T., Groves, B., Kuchina, A., Rosenberg, A.B., Jojic, N., Fields, S.,Seelig, G., 2017. Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences. Genome Res. 27, 2015-2024
    Fernoaga, V., Sandu, V.,Balan, T., 2020. Artificial intelligence for the prediction of exhaust back pressure effect on the performance of diesel engines. Appli. Sci. 10,7370
    Gaudelli, N.M., Komor, A.C., Rees, H.A., Packer, M.S., Badran, A.H., Bryson, D.I.,Liu, D.R., 2017. Programmable base editing of A*T to G*C in genomic DNA without DNA cleavage. Nature 551, 464-471
    Hui, K.K., Kim, Y., Lee, S., Min, S., Bae, J.Y., Choi, J.W., Park, J., Jung, D., Yoon, S., Kim, H.H. 2019. SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Sci. Adv. 5, eaax9249
    Jensen, K.T., Floe, L., Petersen, T.S., Huang, J., Xu, F., Bolund, L., Luo, Y.,Lin, L., 2017. Chromatin accessibility and guide sequence secondary structure affect CRISPR-Cas9 gene editing efficiency. FEBS Lett. 591, 1892-1901
    Joung, J., Konermann, S., Gootenberg, J.S., Abudayyeh, O.O., Platt, R.J., Brigham, M.D., Sanjana, N.E.,Zhang, F., 2017. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828-863
    Kim, H.K., Min, S., Song, M., Jung, S., Choi, J.W., Kim, Y., Lee, S., Yoon, S.,Kim, H.H., 2018. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239-241
    Komor, A.C., Kim, Y.B., Packer, M.S., Zuris, J.A.,Liu, D.R., 2016. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420-424
    Kurt, I.C., Zhou, R., Iyer, S., Garcia, S.P., Miller, B.R., Langner, L.M., Grunewald, J.,Joung, J.K., 2021. CRISPR C-to-G base editors for inducing targeted DNA transversions in human cells. Nat. Biotechnol. 39, 41-46
    Li, W., Singh, P.K., Sowd, G.A., Bedwell, G.J., Jang, S., Achuthan, V., Oleru, A.V., Wong, D., Fadel, H.J., Lee, K.E., et l., 2020. CPSF6-dependent targeting of speckle-associated domains distinguishes primate from nonprimate lentiviral integration. mBio. 11, e02254-20
    Nishida, K., Arazoe, T., Yachie, N., Banno, S., Kakimoto, M., Tabata, M., Mochizuki, M., Miyabe, A., Araki, M., Hara, K.Y., et l., 2016. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 353, aaf8729
    Sanjana, N.E., Shalem, O.,Zhang, F., 2014. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods. 11, 783-784
    Shalem, O., Sanjana, N.E., Hartenian, E., Shi, X., Scott, D.A., Mikkelsen, T.S., Heckl, D., Ebert, B.L., Root, D.E.,Doench, J.G., Zhang, F., 2014. Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. Science 343, 84-87
    Smibi, M.J.,Menon, V., 2019. Modeling compensation of data science professionals in BRIC nations, in:Abraham, A., Dutta, P., Mandal, J.K., Bhattacharya, A., Dutta, S., (Eds.), Emerging Technologies in Data Mining and Information Security emerging technologies in data mining and information security. Springer Singapore., Singapore, pp. 631-638
    Song, M., Kim, H.K., Lee, S., Kim, Y., Seo, S.Y., Park, J., Choi, J.W., Jang, H., Shin, J.H., Min, S., et l., 2020. Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nat. Biotechnol. 38, 1037-1043
    Zhao, D.D., Li, J., Li, S.W., Xin, X.Q., Hu, M., Price, M.A., Rosser, S.J., Bi, C.H., Zhang, X.L., 2021. Glycosylase base editors enable C-to-A and C-to-G base changes. Nat. Biotechnol. 39, 35-40
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (296) PDF downloads (42) Cited by ()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return