A PLATFORM TO AID SELECT THE OPTIMAL TOOL TO DESIGN GUIDE RNAS

Highlights

● Summaries on sgRNAs design.

● Overview of the features of 43 web sgRNA designers.

● A platform to select optimal sgRNA design tool.

Abstract

CRISPR-mediated gene-editing technology has arguably driven an unprecedented revolution in biological sciences for its role in elucidating gene functions. A multitude of software has been developed for the design and analysis of CRISPR/Cas experiments, including predictive tools to design optimally guide RNA for various experimental operations. Different in silico sgRNA design tools have various application scenarios and identifying the optimal design tools can often be a challenge. This paper describes the sgRNA design workflow in experiments, the classification of sgRNA designers, previously published benchmarking work of in silico designers, and the criteria involved how to select an sgRNA web server. Through basic testing, this paper comprehensively overviews and compares the features of 43 web server designers to provide a reference for the readers. Ultimately, the project developed an integrated platform, called Aid-TG, which helps users find appropriate tools quickly.

Graphical abstract

Keywords

CRISPR/Cas / Aid-TG / gene editing / sgRNA design / web server

References

[1] Knott G J, Doudna J A . CRISPR-Cas guides the future of genetic engineering. Science, 2018, 361(6405): 866–869

[2] Gilbert L A, Larson M H, Morsut L, Liu Z, Brar G A, Torres S E, Stern-Ginossar N, Brandman O, Whitehead E H, Doudna J A, Lim W A, Weissman J S, Qi L S . CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell, 2013, 154(2): 442–451

[3] Jiang F, Doudna J A . CRISPR-Cas9 structures and mechanisms. Annual Review of Biophysics, 2017, 46(1): 505–529

[4] Hanna R E, Doench J G . Design and analysis of CRISPR-Cas experiments. Nature Biotechnology, 2020, 38(7): 813–823

[5] Zhang C, Quan R, Wang J . Development and application of CRISPR/Cas9 technologies in genomic editing. Human Molecular Genetics, 2018, 27(R2): R79–R88

[6] Trevino A E, Zhang F . Genome editing using Cas9 nickases. Methods in Enzymology, 2014, 546: 161–174

[7] Kazi T A, Biswas S R . CRISPR/dCas system as the modulator of gene expression. Progress in Molecular Biology and Translational Science, 2021, 178: 99–122

[8] Nuñez J K, Chen J, Pommier G C, Cogan J Z, Replogle J M, Adriaens C, Ramadoss G N, Shi Q, Hung K L, Samelson A J, Pogson A N, Kim J Y S, Chung A, Leonetti M D, Chang H Y, Kampmann M, Bernstein B E, Hovestadt V, Gilbert L A, Weissman J S . Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. Cell, 2021, 184(9): 2503–2519.e17

[9] Paul B, Montoya G . CRISPR-Cas12a: functional overview and applications. Biomedical Journal, 2020, 43(1): 8–17

[10] Cox D B T, Gootenberg J S, Abudayyeh O O, Franklin B, Kellner M J, Joung J, Zhang F . RNA editing with CRISPR-Cas13. Science, 2017, 358(6366): 1019–1027

[11] Doench J G, Hartenian E, Graham D B, Tothova Z, Hegde M, Smith I, Sullender M, Ebert B L, Xavier R J, Root D E . Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nature Biotechnology, 2014, 32(12): 1262–1267

[12] Zhang Y, Zhao G, Ahmed F Y H, Yi T, Hu S, Cai T, Liao Q . In silico method in CRISPR/Cas system: an expedite and powerful booster. Frontiers in Oncology, 2020, 10: 584404

[13] Doench J G, Fusi N, Sullender M, Hegde M, Vaimberg E W, Donovan K F, Smith I, Tothova Z, Wilen C, Orchard R, Virgin H W, Listgarten J, Root D E . Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 2016, 34(2): 184–191

[14] 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. Genome Biology, 2018, 19(1): 80

[15] Kim H K, Min S, Song M, Jung S, Choi J W, Kim Y, Lee S, Yoon S, Kim H H . Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nature Biotechnology, 2018, 36(3): 239–241

[16] Liu Q, Cheng X, Liu G, Li B, Liu X . Deep learning improves the ability of sgRNA off-target propensity prediction. BMC Bioinformatics, 2020, 21(1): 51

[17] Wong N, Liu W, Wang X . WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system. Genome Biology, 2015, 16(1): 218

[18] Labun K, Montague T G, Krause M, Torres Cleuren Y N, Tjeldnes H, Valen E . CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing. Nucleic Acids Research, 2019, 47(W1): W171–W174

[19] Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo J L . CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One, 2015, 10(4): e0124633

[20] Xu H, Xiao T, Chen C H, Li W, Meyer C A, Wu Q, Wu D, Cong L, Zhang F, Liu J S, Brown M, Liu X S . Sequence determinants of improved CRISPR sgRNA design. Genome Research, 2015, 25(8): 1147–1157

[21] Qianqian Y, Lei M. A platform to aid select the optimal tool to design guide RNAs. Available at Aid-TG website on April 20, 2022

[22] Yan J, Xue D, Chuai G, Gao Y, Zhang G, Liu Q . Benchmarking and integrating genome-wide CRISPR off-target detection and prediction. Nucleic Acids Research, 2020, 48(20): 11370–11379

[23] O’Brien A, Bailey T L . GT-Scan: identifying unique genomic targets. Bioinformatics, 2014, 30(18): 2673–2675

[24] Park J, Bae S, Kim J S . Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites. Bioinformatics, 2015, 31(24): 4014–4016

[25] Hwang G H, Park J, Lim K, Kim S, Yu J, Yu E, Kim S T, Eils R, Kim J S, Bae S . Web-based design and analysis tools for CRISPR base editing. BMC Bioinformatics, 2018, 19(1): 542

[26] 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(10): 1473–1475

[27] Hwang G H, Jeong Y K, Habib O, Hong S A, Lim K, Kim J S, Bae S . PE-Designer and PE-Analyzer: web-based design and analysis tools for CRISPR prime editing. Nucleic Acids Research, 2021, 49(W1): W499–W504

[28] He C, Liu H, Chen D, Xie W Z, Wang M, Li Y, Gong X, Yan W, Chen L L . CRISPR-Cereal: a guide RNA design tool integrating regulome and genomic variation for wheat, maize and rice. Plant Biotechnology Journal, 2021, 19(11): 2141–2143

[29] Oliveros J C, Franch M, Tabas-Madrid D, San-León D, Montoliu L, Cubas P, Pazos F . Breaking-Cas-interactive design of guide RNAs for CRISPR-Cas experiments for ENSEMBL genomes. Nucleic Acids Research, 2016, 44(W1): W267–W271

[30] Chow R D, Chen J S, Shen J, Chen S . A web tool for the design of prime-editing guide RNAs. Nature Biomedical Engineering, 2021, 5(2): 190–194

[31] Minkenberg B, Zhang J, Xie K, Yang Y . CRISPR-PLANT v2: an online resource for highly specific guide RNA spacers based on improved off-target analysis. Plant Biotechnology Journal, 2019, 17(1): 5–8

[32] Gratz S J, Ukken F P, Rubinstein C D, Thiede G, Donohue L K, Cummings A M, O’Connor-Giles K M . Highly specific and efficient CRISPR/Cas9-catalyzed homology-directed repair in Drosophila. Genetics, 2014, 196(4): 961–971

[33] Blin K, Shaw S, Tong Y, Weber T . Designing sgRNAs for CRISPR-BEST base editing applications with CRISPy-web 2.0. Synthetic and Systems Biotechnology, 2020, 5(2): 99–102

[34] Heigwer F, Kerr G, Boutros M . E-CRISP: fast CRISPR target site identification. Nature Methods, 2014, 11(2): 122–123

[35] Pliatsika V, Rigoutsos I . “Off-Spotter”: very fast and exhaustive enumeration of genomic lookalikes for designing CRISPR/Cas guide RNAs. Biology Direct, 2015, 10(1): 4

[36] Moreno-Mateos M A, Vejnar C E, Beaudoin J D, Fernandez J P, Mis E K, Khokha M K, Giraldez A J . CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods, 2015, 12(10): 982–988

[37] Prykhozhij S V, Rajan V, Gaston D, Berman J N . 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(3): e0119372

[38] Sander J D, Joung J K . CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology, 2014, 32(4): 347–355

[39] Zhang T, Gao Y, Wang R, Zhao Y . Production of guide RNAs in vitro and in vivo for CRISPR using ribozymes and RNA polymerase II promoters. Bio-Protocol, 2017, 7(4): e2148

[40] Hodgkins A, Farne A, Perera S, Grego T, Parry-Smith D J, Skarnes W C, Iyer V . WGE: a CRISPR database for genome engineering. Bioinformatics, 2015, 31(18): 3078–3080

[41] Liu H, Wei Z, Dominguez A, Li Y, Wang X, Qi L S . CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics, 2015, 31(22): 3676–3678

[42] Zhu H, Richmond E, Liang C . CRISPR-RT: a web application for designing CRISPR-C2c2 crRNA with improved target specificity. Bioinformatics, 2018, 34(1): 117–119

[43] Xie X, Ma X, Zhu Q, Zeng D, Li G, Liu Y G . CRISPR-GE: a convenient software toolkit for CRISPR-based genome editing. Molecular Plant, 2017, 10(9): 1246–1249

[44] Perez A R, Pritykin Y, Vidigal J A, Chhangawala S, Zamparo L, Leslie C S, Ventura A . GuideScan software for improved single and paired CRISPR guide RNA design. Nature Biotechnology, 2017, 35(4): 347–349

[45] Lei Y, Lu L, Liu H Y, Li S, Xing F, Chen L L . CRISPR-P: a web tool for synthetic single-guide RNA design of CRISPR-system in plants. Molecular Plant, 2014, 7(9): 1494–1496

[46] Concordet J P, Haeussler M . CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Research, 2018, 46(W1): W242–W245

[47] Allen F, Crepaldi L, Alsinet C, Strong A J, Kleshchevnikov V, De Angeli P, Páleníková P, Khodak A, Kiselev V, Kosicki M, Bassett A R, Harding H, Galanty Y, Muñoz-Martínez F, Metzakopian E, Jackson S P, Parts L . Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nature Biotechnology, 2019, 37(1): 64–72

[48] Chari R, Mali P, Moosburner M, Church G M . Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nature Methods, 2015, 12(9): 823–826

[49] Wilms D, Adler Y, Schröer F, Bunnemann L, Schmidt S . Elastic modulus distribution in poly(N-isopopylacrylamide) and oligo(ethylene glycol methacrylate)-based microgels studied by AFM. Soft Matter, 2021, 17(23): 5711–5717

[50] Billon P, Bryant E E, Joseph S A, Nambiar T S, Hayward S B, Rothstein R, Ciccia A . CRISPR-mediated base editing enables efficient disruption of eukaryotic genes through induction of STOP codons. Molecular Cell, 2017, 67(6): 1068–1079.e4

[51] Cornean A, Gierten J, Welz B, Mateo J L, Thumberger T, Wittbrodt J . Precise in vivo functional analysis of DNA variants with base editing using ACEofBASEs target prediction. eLife, 2022, 11: e72124

[52] Wang D, Zhang C, Wang B, Li B, Wang Q, Liu D, Wang H, Zhou Y, Shi L, Lan F, Wang Y . Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning. Nature Communications, 2019, 10(1): 4284

[53] Shen M W, Arbab M, Hsu J Y, Worstell D, Culbertson S J, Krabbe O, Cassa C A, Liu D R, Gifford D K, Sherwood R I . Predictable and precise template-free CRISPR editing of pathogenic variants. Nature, 2018, 563(7733): 646–651

[54] Arbab M, Shen M W, Mok B, Wilson C, Matuszek Ż, Cassa C A, Liu D R . Determinants of base editing outcomes from target library analysis and machine learning. Cell, 2020, 182(2): 463–480.e30

[55] Chari R, Yeo N C, Chavez A, Church G M . sgRNA Scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity. ACS Synthetic Biology, 2017, 6(5): 902–904

[56] Kim H K, Kim Y, Lee S, Min S, Bae J Y, Choi J W, Park J, Jung D, Yoon S, Kim H H . SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Science Advance, 2019, 5(11): eaax9249

[57] Yuan T, Yan N, Fei T, Zheng J, Meng J, Li N, Liu J, Zhang H, Xie L, Ying W, Li D, Shi L, Sun Y, Li Y, Li Y, Sun Y, Zuo E . Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods. Nature Communications, 2021, 12(1): 4902

[58] Pulido-Quetglas C, Aparicio-Prat E, Arnan C, Polidori T, Hermoso T, Palumbo E, Ponomarenko J, Guigo R, Johnson R . Scalable design of paired CRISPR guide RNAs for genomic deletion. PLoS Computational Biology, 2017, 13(3): e1005341

[59] Naito Y, Hino K, Bono H, Ui-Tei K . CRISPRdirect: software for designing CRISPR/Cas guide RNA with reduced off-target sites. Bioinformatics, 2015, 31(7): 1120–1123

[60] Peng D, Tarleton R . EuPaGDT: a web tool tailored to design CRISPR guide RNAs for eukaryotic pathogens. Microbial Genomics, 2015, 1(4): e000033

[61] Xiong Y, Xie X, Wang Y, Ma W, Liang P, Songyang Z, Dai Z . pgRNAFinder: a web-based tool to design distance independent paired-gRNA. Bioinformatics, 2017, 33(22): 3642–3644

[62] Wilson L O W, Reti D, O’Brien A R, Dunne R A, Bauer D C . High activity target-site identification using phenotypic independent CRISPR-Cas9 core functionality. CRISPR Journal, 2018, 1(2): 182–190

[63] Song M, Kim H K, Lee S, Kim Y, Seo S Y, Park J, Choi J W, Jang H, Shin J H, Min S, Quan Z, Kim J H, Kang H C, Yoon S, Kim H H . Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nature Biotechnology, 2020, 38(9): 1037–1043

[64] Marquart K F, Allam A, Janjuha S, Sintsova A, Villiger L, Frey N, Krauthammer M, Schwank G . Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nature Communications, 2021, 12(1): 5114

[65] Moreb E A, Lynch M D . Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity. Nature Communications, 2021, 12(1): 5034

[66] Meier J A, Zhang F, Sanjana N E . GUIDES: sgRNA design for loss-of-function screens. Nature Methods, 2017, 14(9): 831–832

[67] Alkan F, Wenzel A, Anthon C, Havgaard J H, Gorodkin J . CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters. Genome Biology, 2018, 19(1): 177

[68] 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. Briefings in Bioinformatics, 2018, 19(4): 721–724

[69] Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud J B, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J, Joly J S, Concordet J P . Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology, 2016, 17(1): 148

[70] Blin K, Pedersen L E, Weber T, Lee S Y . CRISPy-web: an online resource to design sgRNAs for CRISPR applications. Synthetic and Systems Biotechnology, 2016, 1(2): 118–121

[71] Chu V T, Graf R, Wirtz T, Weber T, Favret J, Li X, Petsch K, Tran N T, Sieweke M H, Berek C, Kühn R, Rajewsky K . Efficient CRISPR-mediated mutagenesis in primary immune cells using CrispRGold and a C57BL/6 Cas9 transgenic mouse line. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(44): 12514–12519

Compliance with ethics guidelines

Qianqian Yang and Lei Ma declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2022. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)