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

doi:10.15302/J-FASE-2022479

Frontiers of Agricultural Science and Engineering

, Vol.

Issue (): 0 https://doi.org/10.15302/J-FASE-2022479

本期目录

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

Qianqian YANG, Lei MA(

)

Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China

全文: PDF(3040 KB) HTML

Abstract：

● Summaries on sgRNAs design.

● Overview of the features of 43 web sgRNA designers.

● A platform to select optimal sgRNA design tool.

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.

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

收稿日期: 2022-07-15

Corresponding Author(s): Lei MA

引用本文:

. [J]. Frontiers of Agricultural Science and Engineering, 10.15302/J-FASE-2022479.
Qianqian YANG, Lei MA. A PLATFORM TO AID SELECT THE OPTIMAL TOOL TO DESIGN GUIDE RNAS. Front. Agr. Sci. Eng. , , (): 0.

链接本文:

https://academic.hep.com.cn/fase/CN/10.15302/J-FASE-2022479
https://academic.hep.com.cn/fase/CN/Y/V/I/0

Fig.1

Tab.1

Tab.2

Tab.3

Fig.2

1	G J, Knott J A Doudna . CRISPR-Cas guides the future of genetic engineering. Science, 2018, 361(6405): 866–869 https://doi.org/10.1126/science.aat5011 pmid: 30166482
2	L A, Gilbert M H, Larson L, Morsut Z, Liu G A, Brar S E, Torres N, Stern-Ginossar O, Brandman E H, Whitehead J A, Doudna W A, Lim J S, Weissman L S Qi . CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell, 2013, 154(2): 442–451 https://doi.org/10.1016/j.cell.2013.06.044 pmid: 23849981
3	F, Jiang J A Doudna . CRISPR-Cas9 structures and mechanisms. Annual Review of Biophysics, 2017, 46(1): 505–529 https://doi.org/10.1146/annurev-biophys-062215-010822 pmid: 28375731
4	R E, Hanna J G Doench . Design and analysis of CRISPR-Cas experiments. Nature Biotechnology, 2020, 38(7): 813–823 https://doi.org/10.1038/s41587-020-0490-7 pmid: 32284587
5	C, Zhang R, Quan J Wang . Development and application of CRISPR/Cas9 technologies in genomic editing. Human Molecular Genetics, 2018, 27(R2): R79–R88 https://doi.org/10.1093/hmg/ddy120 pmid: 29659822
6	A E, Trevino F Zhang . Genome editing using Cas9 nickases. Methods in Enzymology, 2014, 546: 161–174 https://doi.org/10.1016/B978-0-12-801185-0.00008-8 pmid: 25398340
7	T A, Kazi S R Biswas . CRISPR/dCas system as the modulator of gene expression. Progress in Molecular Biology and Translational Science, 2021, 178: 99–122 https://doi.org/10.1016/bs.pmbts.2020.12.002 pmid: 33685602
8	J K, Nuñez J, Chen G C, Pommier J Z, Cogan J M, Replogle C, Adriaens G N, Ramadoss Q, Shi K L, Hung A J, Samelson A N, Pogson J Y S, Kim A, Chung M D, Leonetti H Y, Chang M, Kampmann B E, Bernstein V, Hovestadt L A, Gilbert J S Weissman . Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. Cell, 2021, 184(9): 2503–2519.e17 https://doi.org/10.1016/j.cell.2021.03.025 pmid: 33838111
9	B, Paul G Montoya . CRISPR-Cas12a: functional overview and applications. Biomedical Journal, 2020, 43(1): 8–17 https://doi.org/10.1016/j.bj.2019.10.005 pmid: 32200959
10	D B T, Cox J S, Gootenberg O O, Abudayyeh B, Franklin M J, Kellner J, Joung F Zhang . RNA editing with CRISPR-Cas13. Science, 2017, 358(6366): 1019–1027 https://doi.org/10.1126/science.aaq0180 pmid: 29070703
11	J G, Doench E, Hartenian D B, Graham Z, Tothova M, Hegde I, Smith M, Sullender B L, Ebert R J, Xavier D E Root . Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nature Biotechnology, 2014, 32(12): 1262–1267 https://doi.org/10.1038/nbt.3026 pmid: 25184501
12	Y, Zhang G, Zhao F Y H, Ahmed T, Yi S, Hu T, Cai Q Liao . In silico method in CRISPR/Cas system: an expedite and powerful booster. Frontiers in Oncology, 2020, 10: 584404 https://doi.org/10.3389/fonc.2020.584404 pmid: 33123486
13	J G, Doench N, Fusi M, Sullender M, Hegde E W, Vaimberg K F, Donovan I, Smith Z, Tothova C, Wilen R, Orchard H W, Virgin J, Listgarten D E Root . Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 2016, 34(2): 184–191 https://doi.org/10.1038/nbt.3437 pmid: 26780180
14	G, Chuai H, Ma J, Yan M, Chen N, Hong D, Xue C, Zhou C, Zhu K, Chen B, Duan F, Gu S, Qu D, Huang J, Wei Q Liu . DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biology, 2018, 19(1): 80 https://doi.org/10.1186/s13059-018-1459-4 pmid: 29945655
15	H K, Kim S, Min M, Song S, Jung J W, Choi Y, Kim S, Lee S, Yoon H H Kim . Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nature Biotechnology, 2018, 36(3): 239–241 https://doi.org/10.1038/nbt.4061 pmid: 29431740
16	Q, Liu X, Cheng G, Liu B, Li X Liu . Deep learning improves the ability of sgRNA off-target propensity prediction. BMC Bioinformatics, 2020, 21(1): 51 https://doi.org/10.1186/s12859-020-3395-z pmid: 32041517
17	N, Wong W, Liu X Wang . WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system. Genome Biology, 2015, 16(1): 218 https://doi.org/10.1186/s13059-015-0784-0 pmid: 26521937
18	K, Labun T G, Montague M, Krause Cleuren Y N, Torres H, Tjeldnes E Valen . CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing. Nucleic Acids Research, 2019, 47(W1): W171–W174 https://doi.org/10.1093/nar/gkz365 pmid: 31106371
19	M, Stemmer T, Thumberger Sol Keyer M, Del J, Wittbrodt J L Mateo . CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One, 2015, 10(4): e0124633 https://doi.org/10.1371/journal.pone.0124633 pmid: 25909470
20	H, Xu T, Xiao C H, Chen W, Li C A, Meyer Q, Wu D, Wu L, Cong F, Zhang J S, Liu M, Brown X S Liu . Sequence determinants of improved CRISPR sgRNA design. Genome Research, 2015, 25(8): 1147–1157 https://doi.org/10.1101/gr.191452.115 pmid: 26063738
21	Y, Qianqian M Lei . A platform to aid select the optimal tool to design guide RNAs. Available at Aid-TG website on April 20, 2022
22	J, Yan D, Xue G, Chuai Y, Gao G, Zhang Q Liu . Benchmarking and integrating genome-wide CRISPR off-target detection and prediction. Nucleic Acids Research, 2020, 48(20): 11370–11379 https://doi.org/10.1093/nar/gkaa930 pmid: 33137817
23	A, O’Brien T L Bailey . GT-Scan: identifying unique genomic targets. Bioinformatics, 2014, 30(18): 2673–2675 https://doi.org/10.1093/bioinformatics/btu354 pmid: 24860161
24	J, Park S, Bae J S Kim . Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites. Bioinformatics, 2015, 31(24): 4014–4016 pmid: 26358729
25	G H, Hwang J, Park K, Lim S, Kim J, Yu E, Yu S T, Kim R, Eils J S, Kim S Bae . Web-based design and analysis tools for CRISPR base editing. BMC Bioinformatics, 2018, 19(1): 542 https://doi.org/10.1186/s12859-018-2585-4 pmid: 30587106
26	S, Bae J, Park J S Kim . 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 https://doi.org/10.1093/bioinformatics/btu048 pmid: 24463181
27	G H, Hwang Y K, Jeong O, Habib S A, Hong K, Lim J S, Kim S Bae . PE-Designer and PE-Analyzer: web-based design and analysis tools for CRISPR prime editing. Nucleic Acids Research, 2021, 49(W1): W499–W504 https://doi.org/10.1093/nar/gkab319 pmid: 33939828
28	C, He H, Liu D, Chen W Z, Xie M, Wang Y, Li X, Gong W, Yan L L Chen . 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 https://doi.org/10.1111/pbi.13675 pmid: 34310056
29	J C, Oliveros M, Franch D, Tabas-Madrid D, San-León L, Montoliu P, Cubas F Pazos . Breaking-Cas-interactive design of guide RNAs for CRISPR-Cas experiments for ENSEMBL genomes. Nucleic Acids Research, 2016, 44(W1): W267–W271 https://doi.org/10.1093/nar/gkw407 pmid: 27166368
30	R D, Chow J S, Chen J, Shen S Chen . A web tool for the design of prime-editing guide RNAs. Nature Biomedical Engineering, 2021, 5(2): 190–194 https://doi.org/10.1038/s41551-020-00622-8 pmid: 32989284
31	B, Minkenberg J, Zhang K, Xie Y Yang . 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 https://doi.org/10.1111/pbi.13025 pmid: 30325102
32	S J, Gratz F P, Ukken C D, Rubinstein G, Thiede L K, Donohue A M, Cummings K M O’Connor-Giles . Highly specific and efficient CRISPR/Cas9-catalyzed homology-directed repair in Drosophila. Genetics, 2014, 196(4): 961–971 https://doi.org/10.1534/genetics.113.160713 pmid: 24478335
33	K, Blin S, Shaw Y, Tong T Weber . Designing sgRNAs for CRISPR-BEST base editing applications with CRISPy-web 2.0. Synthetic and Systems Biotechnology, 2020, 5(2): 99–102 https://doi.org/10.1016/j.synbio.2020.05.005 pmid: 32596519
34	F, Heigwer G, Kerr M Boutros . E-CRISP: fast CRISPR target site identification. Nature Methods, 2014, 11(2): 122–123 https://doi.org/10.1038/nmeth.2812 pmid: 24481216
35	V, Pliatsika I Rigoutsos . “Off-Spotter”: very fast and exhaustive enumeration of genomic lookalikes for designing CRISPR/Cas guide RNAs. Biology Direct, 2015, 10(1): 4 https://doi.org/10.1186/s13062-015-0035-z pmid: 25630343
36	M A, Moreno-Mateos C E, Vejnar J D, Beaudoin J P, Fernandez E K, Mis M K, Khokha A J Giraldez . CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods, 2015, 12(10): 982–988 https://doi.org/10.1038/nmeth.3543 pmid: 26322839
37	S V, Prykhozhij V, Rajan D, Gaston J N Berman . 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 https://doi.org/10.1371/journal.pone.0119372 pmid: 25742428
38	J D, Sander J K Joung . CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology, 2014, 32(4): 347–355 https://doi.org/10.1038/nbt.2842 pmid: 24584096
39	T, Zhang Y, Gao R, Wang Y Zhao . Production of guide RNAs in vitro and in vivo for CRISPR using ribozymes and RNA polymerase II promoters. Bio-Protocol, 2017, 7(4): e2148 https://doi.org/10.21769/BioProtoc.2148 pmid: 28603751
40	A, Hodgkins A, Farne S, Perera T, Grego D J, Parry-Smith W C, Skarnes V Iyer . WGE: a CRISPR database for genome engineering. Bioinformatics, 2015, 31(18): 3078–3080 https://doi.org/10.1093/bioinformatics/btv308 pmid: 25979474
41	H, Liu Z, Wei A, Dominguez Y, Li X, Wang L S Qi . CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics, 2015, 31(22): 3676–3678 https://doi.org/10.1093/bioinformatics/btv423 pmid: 26209430
42	H, Zhu E, Richmond C Liang . CRISPR-RT: a web application for designing CRISPR-C2c2 crRNA with improved target specificity. Bioinformatics, 2018, 34(1): 117–119 https://doi.org/10.1093/bioinformatics/btx580 pmid: 28968770
43	X, Xie X, Ma Q, Zhu D, Zeng G, Li Y G Liu . CRISPR-GE: a convenient software toolkit for CRISPR-based genome editing. Molecular Plant, 2017, 10(9): 1246–1249 https://doi.org/10.1016/j.molp.2017.06.004 pmid: 28624544
44	A R, Perez Y, Pritykin J A, Vidigal S, Chhangawala L, Zamparo C S, Leslie A Ventura . GuideScan software for improved single and paired CRISPR guide RNA design. Nature Biotechnology, 2017, 35(4): 347–349 https://doi.org/10.1038/nbt.3804 pmid: 28263296
45	Y, Lei L, Lu H Y, Liu S, Li F, Xing L L Chen . CRISPR-P: a web tool for synthetic single-guide RNA design of CRISPR-system in plants. Molecular Plant, 2014, 7(9): 1494–1496 https://doi.org/10.1093/mp/ssu044 pmid: 24719468
46	J P, Concordet M Haeussler . CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Research, 2018, 46(W1): W242–W245 https://doi.org/10.1093/nar/gky354 pmid: 29762716
47	F, Allen L, Crepaldi C, Alsinet A J, Strong V, Kleshchevnikov Angeli P, De P, Páleníková A, Khodak V, Kiselev M, Kosicki A R, Bassett H, Harding Y, Galanty F, Muñoz-Martínez E, Metzakopian S P, Jackson L Parts . Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nature Biotechnology, 2019, 37(1): 64–72 https://doi.org/10.1038/nbt.4317 pmid: 30480667
48	R, Chari P, Mali M, Moosburner G M Church . Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nature Methods, 2015, 12(9): 823–826 https://doi.org/10.1038/nmeth.3473 pmid: 26167643
49	D, Wilms Y, Adler F, Schröer L, Bunnemann S Schmidt . Elastic modulus distribution in poly(N-isopopylacrylamide) and oligo(ethylene glycol methacrylate)-based microgels studied by AFM. Soft Matter, 2021, 17(23): 5711–5717 https://doi.org/10.1039/D1SM00291K pmid: 34013309
50	P, Billon E E, Bryant S A, Joseph T S, Nambiar S B, Hayward R, Rothstein A Ciccia . CRISPR-mediated base editing enables efficient disruption of eukaryotic genes through induction of STOP codons. Molecular Cell, 2017, 67(6): 1068–1079.e4 https://doi.org/10.1016/j.molcel.2017.08.008 pmid: 28890334
51	A, Cornean J, Gierten B, Welz J L, Mateo T, Thumberger J Wittbrodt . Precise in vivo functional analysis of DNA variants with base editing using ACEofBASEs target prediction. eLife, 2022, 11: e72124 https://doi.org/10.7554/eLife.72124 pmid: 35373735
52	D, Wang C, Zhang B, Wang B, Li Q, Wang D, Liu H, Wang Y, Zhou L, Shi F, Lan Y Wang . Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning. Nature Communications, 2019, 10(1): 4284 https://doi.org/10.1038/s41467-019-12281-8 pmid: 31537810
53	M W, Shen M, Arbab J Y, Hsu D, Worstell S J, Culbertson O, Krabbe C A, Cassa D R, Liu D K, Gifford R I Sherwood . Predictable and precise template-free CRISPR editing of pathogenic variants. Nature, 2018, 563(7733): 646–651 https://doi.org/10.1038/s41586-018-0686-x pmid: 30405244
54	M, Arbab M W, Shen B, Mok C, Wilson Ż, Matuszek C A, Cassa D R Liu . Determinants of base editing outcomes from target library analysis and machine learning. Cell, 2020, 182(2): 463–480.e30 https://doi.org/10.1016/j.cell.2020.05.037 pmid: 32533916
55	R, Chari N C, Yeo A, Chavez G M Church . sgRNA Scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity. ACS Synthetic Biology, 2017, 6(5): 902–904 https://doi.org/10.1021/acssynbio.6b00343 pmid: 28146356
56	H K, Kim Y, Kim S, Lee S, Min J Y, Bae J W, Choi J, Park D, Jung S, Yoon H H Kim . SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Science Advance, 2019, 5(11): eaax9249
57	T, Yuan N, Yan T, Fei J, Zheng J, Meng N, Li J, Liu H, Zhang L, Xie W, Ying D, Li L, Shi Y, Sun Y, Li Y, Li Y, Sun E Zuo . Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods. Nature Communications, 2021, 12(1): 4902 https://doi.org/10.1038/s41467-021-25217-y pmid: 34385461
58	C, Pulido-Quetglas E, Aparicio-Prat C, Arnan T, Polidori T, Hermoso E, Palumbo J, Ponomarenko R, Guigo R Johnson . Scalable design of paired CRISPR guide RNAs for genomic deletion. PLoS Computational Biology, 2017, 13(3): e1005341 https://doi.org/10.1371/journal.pcbi.1005341 pmid: 28253259
59	Y, Naito K, Hino H, Bono K Ui-Tei . CRISPRdirect: software for designing CRISPR/Cas guide RNA with reduced off-target sites. Bioinformatics, 2015, 31(7): 1120–1123 https://doi.org/10.1093/bioinformatics/btu743 pmid: 25414360
60	D, Peng R Tarleton . EuPaGDT: a web tool tailored to design CRISPR guide RNAs for eukaryotic pathogens. Microbial Genomics, 2015, 1(4): e000033 https://doi.org/10.1099/mgen.0.000033 pmid: 28348817
61	Y, Xiong X, Xie Y, Wang W, Ma P, Liang Z, Songyang Z Dai . pgRNAFinder: a web-based tool to design distance independent paired-gRNA. Bioinformatics, 2017, 33(22): 3642–3644 https://doi.org/10.1093/bioinformatics/btx472 pmid: 28961776
62	L O W, Wilson D, Reti A R, O’Brien R A, Dunne D C Bauer . High activity target-site identification using phenotypic independent CRISPR-Cas9 core functionality. CRISPR Journal, 2018, 1(2): 182–190 https://doi.org/10.1089/crispr.2017.0021 pmid: 31021206
63	M, Song H K, Kim S, Lee Y, Kim S Y, Seo J, Park J W, Choi H, Jang J H, Shin S, Min Z, Quan J H, Kim H C, Kang S, Yoon H H Kim . Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nature Biotechnology, 2020, 38(9): 1037–1043 https://doi.org/10.1038/s41587-020-0573-5 pmid: 32632303
64	K F, Marquart A, Allam S, Janjuha A, Sintsova L, Villiger N, Frey M, Krauthammer G Schwank . Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nature Communications, 2021, 12(1): 5114 https://doi.org/10.1038/s41467-021-25375-z pmid: 34433819
65	E A, Moreb M D Lynch . Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity. Nature Communications, 2021, 12(1): 5034 https://doi.org/10.1038/s41467-021-25339-3 pmid: 34413309
66	J A, Meier F, Zhang N E Sanjana . GUIDES: sgRNA design for loss-of-function screens. Nature Methods, 2017, 14(9): 831–832 https://doi.org/10.1038/nmeth.4423 pmid: 28858339
67	F, Alkan A, Wenzel C, Anthon J H, Havgaard J Gorodkin . CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters. Genome Biology, 2018, 19(1): 177 https://doi.org/10.1186/s13059-018-1534-x pmid: 30367669
68	J, Yan G, Chuai C, Zhou C, Zhu J, Yang C, Zhang F, Gu H, Xu J, Wei Q Liu . Benchmarking CRISPR on-target sgRNA design. Briefings in Bioinformatics, 2018, 19(4): 721–724 https://doi.org/10.1093/bib/bbx001 pmid: 28203699
69	M, Haeussler K, Schönig H, Eckert A, Eschstruth J, Mianné J B, Renaud S, Schneider-Maunoury A, Shkumatava L, Teboul J, Kent J S, Joly J P Concordet . Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology, 2016, 17(1): 148 https://doi.org/10.1186/s13059-016-1012-2 pmid: 27380939
70	K, Blin L E, Pedersen T, Weber S Y Lee . CRISPy-web: an online resource to design sgRNAs for CRISPR applications. Synthetic and Systems Biotechnology, 2016, 1(2): 118–121 https://doi.org/10.1016/j.synbio.2016.01.003 pmid: 29062934
71	V T, Chu R, Graf T, Wirtz T, Weber J, Favret X, Li K, Petsch N T, Tran M H, Sieweke C, Berek R, Kühn K Rajewsky . 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 https://doi.org/10.1073/pnas.1613884113 pmid: 27729526

Viewed

Full text

Abstract

Cited

Shared

Discussed