DeepCRISPR: Optimized CRISPR guide RNA design by deep learning

Genome Biology

Volumn 19, Issue 1, 2018, Pages

  Chuai, Guohui ^a   Ma, Hanhui ^c   Yan, Jifang ^a   Chen, Ming ^b   Hong, Nanfang ^a   Xue, Dongyu ^a   Zhou, Chi ^a   Zhu, Chenyu ^a   Chen, Ke ^a   Duan, Bin ^a   Gu, Feng ^d   Qu, Sheng ^a   Huang, Deshuang ^a   Wei, Jia ^b   Liu, Qi ^a  

^a TONGJI UNIVERSITY   (China)

^b ASTRAZENECA   (United Kingdom)

^c SHANGHAITECH UNIVERSITY   (China)

^d WENZHOU MEDICAL UNIVERSITY   (China)

Author keywords

CRISPR system; Deep learning; Gene knockout; Off targets; On targets

Indexed keywords

RNA; SINGLE GUIDE RNA; UNCLASSIFIED DRUG; GUIDE RNA;

ARTICLE; AUTOMATION; CLUSTERED REGULARLY INTERSPACED SHORT PALINDROMIC REPEAT; COMPUTER MODEL; CONCEPTUAL FRAMEWORK; EPIGENETICS; GENE KNOCKOUT; GENE TARGETING; INTERNET; LEARNING STYLE; MATHEMATICAL MODEL; PREDICTION; PROCESS OPTIMIZATION; RNA SEQUENCE; BIOLOGY; CELL LINE; COMPUTER SIMULATION; CRISPR CAS SYSTEM; GENETICS; HCT 116 CELL LINE; HEK293 CELL LINE; HELA CELL LINE; HL-60 CELL LINE; HUMAN; MACHINE LEARNING; PROCEDURES; RNA EDITING; TUMOR CELL LINE;

CELL LINE; CELL LINE, TUMOR; CLUSTERED REGULARLY INTERSPACED SHORT PALINDROMIC REPEATS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; CRISPR-CAS SYSTEMS; HCT116 CELLS; HEK293 CELLS; HELA CELLS; HL-60 CELLS; HUMANS; MACHINE LEARNING; RNA EDITING; RNA, GUIDE;

EID: 85049146901     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-018-1459-4     Document Type: Article

References (55)

1. Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, Sullender M, Ebert BL, Xavier RJ, Root DE. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol. 2014;32:1262-7.
2. Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016:34(2):184-91.
3. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819-23.
4. Doudna JA, Charpentier E. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014;346:1258096.
5. Xu H, Xiao T, Chen C-H, Li W, Meyer C, Wu Q, Wu D, Cong L, Zhang F, Liu JS. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 2015:25(8):1147-57.
6. Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013;31:827-32.
7. Chuai G-h, Wang Q-L, Liu Q. In silico meets in vivo: towards computational CRISPR-based sgRNA design. Trends Biotechnol. 2017;35:12-21.
8. Aach J, Mali P, Church GM. CasFinder: Flexible algorithm for identifying specific Cas9 targets in genomes. bioRxiv. 2014:005074. https://doi.org/10.1101/005074.
9. Heigwer F, Kerr G, Boutros M. E-CRISP: fast CRISPR target site identification. Nat Methods. 2014;11:122-3.
10. Labun K, Montague TG, Gagnon JA, Thyme SB, Valen E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016;44(Issue W1):gkw398.
11. Perez AR, Pritykin Y, Vidigal JA, Chhangawala S, Zamparo L, Leslie CS, Ventura A. GuideScan software for improved single and paired CRISPR guide RNA design. Nat Biotechnol. 2017:35(4):347-9.
12. Chari R, Mali P, Moosburner M, Church GM. Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nat Methods. 2015;12:823-6.
13. Chari R, Yeo NC, Chavez A, Church GM. sgRNA scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity. ACS Synth Biol. 2017:6(5):902-4.
14. Moreno-Mateos MA, Vejnar CE, Beaudoin J-D, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015;12:982-8.
15. Yan J, Chuai G, Zhou C, Zhu C, Yang J, Zhang C, Gu F, Xu H, Wei J, Liu Q. Benchmarking CRISPR on-target sgRNA design. Brief Bioinform. 2017:15:1-4.
16. Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud J-B, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016;17:148.
17. Lee CM, Cradick TJ, Fine EJ, Bao G. Nuclease target site selection for maximizing on-target activity and minimizing off-target effects in genome editing. Mol Ther. 2016;24(3):475-87.
18. O'Geen H, Henry IM, Bhakta MS, Meckler JF, Segal DJ. A genome-wide analysis of Cas9 binding specificity using ChIP-seq and targeted sequence capture. Nucleic Acids Res. 2015;43:3389-404.
19. Fu Y, Foden JA, Khayter C, Maeder ML, Reyon D, Joung JK, Sander JD. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat Biotechnol. 2013;31:822-6.
20. Bae S, Park J, Kim J-S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics. 2014;30:1473-5.
21. Ma J, Köster J, Qin Q, Hu S, Li W, Chen C, Cao Q, Wang J, Mei S, Liu Q. CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics. 2016;32:3336-8.
22. Tsai SQ, Zheng Z, Nguyen NT, Liebers M, Topkar VV, Thapar V, Wyvekens N, Khayter C, Iafrate AJ, Le LP. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotechnol. 2015;33:187-97.
23. Kim D, Kim S, Kim S, Park J, Jin-Soo K. Genome-wide target specificities of CRISPR-Cas9 nucleases revealed by multiplex Digenome-seq. Genome Res. 2016:26(3):406-15.
24. Kim D, Bae S, Park J, Kim E, Kim S, Yu HR, Hwang J, Kim J-I, Kim J-S. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat Methods. 2015;12:237-43.
25. Frock RL, Hu J, Meyers RM, Ho Y-J, Kii E, Alt FW. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat Biotechnol. 2015;33:179-86.
26. Ran FA, Cong L, Yan WX, Scott DA, Gootenberg JS, Kriz AJ, Zetsche B, Shalem O, Wu X, Makarova KS. In vivo genome editing using Staphylococcus aureus Cas9. Nature. 2015;520:186-91.
27. Wang X, Wang Y, Wu X, Wang J, Wang Y, Qiu Z, Chang T, Huang H, Lin R-J, Yee J-K. Unbiased detection of off-target cleavage by CRISPR-Cas9 and TALENs using integrase-defective lentiviral vectors. Nat Biotechnol. 2015;33:175-8.
28. Abadi S, Yan WX, Amar D, Mayrose I. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput Biol. 2017;13:e1005807.
29. Peng H, Zheng Y, Blumenstein M, Tao D, Li J. CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling. Bioinformatics. 2018:4:1-9.
30. Kim HK, Min S, Song M, Jung S, Choi JW, Kim Y, Lee S, Yoon S, Kim H. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat Biotechnol. 2018;36:239.
31. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33:831-8.
32. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44.
33. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12:931-4.
34. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35:1798-828.
35. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117.
36. Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell. 2015;163:1515-26.
37. Wang T, Wei JJ, Sabatini DM, Lander ES. Genetic screens in human cells using the CRISPR-Cas9 system. Science. 2014;343:80-4.
38. Prykhozhij SV, Rajan V, Gaston D, Berman JN. CRISPR multitargeter: a web tool to find common and unique CRISPR single guide RNA targets in a set of similar sequences. PLoS One. 2015;10:e0119372.
39. Park J, Bae S, Kim J-S. Cas-designer: a web-based tool for choice of CRISPR-Cas9 target sites. Bioinformatics. 2015;31:4014-6.
40. Wong N, Liu W, Wang X. WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system. Genome Biol. 2015;16:1-8.
41. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 1997;30:1145-59.
42. Listgarten J, Weinstein M, Elibol M, Hoang L, Doench J, Fusi N. Predicting off-target effects for end-to-end CRISPR guide design. bioRxiv. 2016:078253. https://doi.org/10.1101/078253.
43. Labuhn M, Adams FF, Ng M, Knoess S, Schambach A, Charpentier EM, Schwarzer A, Mateo JL, Klusmann J-H, Heckl D. Refined sgRNA efficacy prediction improves large- and small-scale CRISPR-Cas9 applications. Nucleic Acids Res. 2018;46:1375-85.
44. Cho SW, Kim S, Kim Y, Kweon J, Kim HS, Bae S, Kim J-S. Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases. Genome Res. 2014;24:132-41.
45. Singh R, Kuscu C, Quinlan A, Qi Y, Adli M. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 2015;43:e118.
46. Stemmer M, Thumberger T, del Sol Keyer M, Wittbrodt J, Mateo JL. CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One. 2015;10:e0124633.
47. Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps. In: arXiv preprint arXiv:13126034; 2013.
48. Ma H, Tu L-C, Naseri A, Huisman M, Zhang S, Grunwald D, Pederson T. CRISPR-Cas9 nuclear dynamics and target recognition in living cells. J Cell Biol. 2016;214(5):529-37.
49. Wang H, Gu Q, Wei J, Cao Z, Liu Q. Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: where a recommendation system meets genome-wide association studies. Clin Pharmacol Ther. 2015;97:451-4.
50. Badaro G, Hajj H, El-Hajj W, Nachman L. A hybrid approach with collaborative filtering for recommender systems. In: Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International: IEEE; Sardinia, Italy, 2013. p. 349-54.
51. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357-9.
52. Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science. 2004;306:636-40.
53. Upton GJ. Fisher's exact test. J R Stat Soc. 1992;155(3):395-402.
54. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639-45.
55. Chuai G, Ma H, Yan J, Chen M, Hong N, Xue D, Zhou C, Zhu C, Chen K, Duan B, Gu F, Qu S, Huang D, Wei J, Liu Q. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018. https://github.com/bm2-lab/DeepCRISPR.