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  1. NTU Theses and Dissertations Repository
  2. 共同教育中心
  3. 全球農業科技與基因體科學碩士學位學程
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88732
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dc.contributor.advisor伊藤剛zh_TW
dc.contributor.advisorTakeshi Itohen
dc.contributor.author翁明蓮zh_TW
dc.contributor.authorCeline Kurniawanen
dc.date.accessioned2023-08-15T17:33:40Z-
dc.date.available2023-11-09-
dc.date.copyright2023-08-15-
dc.date.issued2023-
dc.date.submitted2023-08-07-
dc.identifier.citationAndrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. In: Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom.
Balch, B. (2021). The future of CRISPR is now. https://www.aamc.org/news/future-crispr-now
Barnett, D. W., Garrison, E. K., Quinlan, A. R., Strömberg, M. P., & Marth, G. T. (2011). BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics, 27(12), 1691-1692. https://doi.org/10.1093/bioinformatics/btr174
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114-2120. https://doi.org/10.1093/bioinformatics/btu170
Casini, A., Olivieri, M., Petris, G., Montagna, C., Reginato, G., Maule, G., Lorenzin, F., Prandi, D., Romanel, A., Demichelis, F., Inga, A., & Cereseto, A. (2018). A highly specific SpCas9 variant is identified by in vivo screening in yeast. Nature Biotechnology, 36(3), 265-271. https://doi.org/10.1038/nbt.4066
Charlier, J., Nadon, R., & Makarenkov, V. (2021). Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing. Bioinformatics. https://doi.org/10.1093/bioinformatics/btab112
Chatterjee, P., Jakimo, N., Lee, J., Amrani, N., Rodríguez, T., Koseki, S. R. T., Tysinger, E., Qing, R., Hao, S., Sontheimer, E. J., & Jacobson, J. (2020). An engineered ScCas9 with broad PAM range and high specificity and activity. Nature Biotechnology, 38(10), 1154-1158. https://doi.org/10.1038/s41587-020-0517-0
Chen, J. S., Dagdas, Y. S., Kleinstiver, B. P., Welch, M. M., Sousa, A. A., Harrington, L. B., Sternberg, S. H., Joung, J. K., Yildiz, A., & Doudna, J. A. (2017). Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature, 550(7676), 407-410. https://doi.org/10.1038/nature24268
Choi, G. C. G., Zhou, P., Yuen, C. T. L., Chan, B. K. C., Xu, F., Bao, S., Chu, H. Y., Thean, D., Tan, K., Wong, K. H., Zheng, Z., & Wong, A. S. L. (2019). Combinatorial mutagenesis en masse optimizes the genome editing activities of SpCas9. Nature Methods, 16(8), 722-730. https://doi.org/10.1038/s41592-019-0473-0
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., & de Hoon, M. J. L. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422-1423. https://doi.org/10.1093/bioinformatics/btp163
Corrigan-Curay, J., O'Reilly, M., Kohn, D. B., Cannon, P. M., Bao, G., Bushman, F. D., Carroll, D., Cathomen, T., Joung, J. K., Roth, D., Sadelain, M., Scharenberg, A. M., von Kalle, C., Zhang, F., Jambou, R., Rosenthal, E., Hassani, M., Singh, A., & Porteus, M. H. (2015). Genome editing technologies: defining a path to clinic. Molecular Therapy, 23(5), 796-806. https://doi.org/10.1038/mt.2015.54
Cradick, T. J., Fine, E. J., Antico, C. J., & Bao, G. (2013). CRISPR/Cas9 systems targeting β-globin and CCR5 genes have substantial off-target activity. Nucleic Acids Research, 41(20), 9584-9592. https://doi.org/10.1093/nar/gkt714
CRISPR beef cattle get FDA green light. (2022). Nature Biotechnology, 40(4), 448-448. https://doi.org/10.1038/s41587-022-01297-z
Crooks, G. E., Hon, G., Chandonia, J. M., & Brenner, S. E. (2004). WebLogo: a sequence logo generator. Genome Research, 14(6), 1188-1190. https://doi.org/10.1101/gr.849004
Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2). https://doi.org/10.1093/gigascience/giab008
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. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), 184-191. https://doi.org/10.1038/nbt.3437
Endo, M., Mikami, M., Endo, A., Kaya, H., Itoh, T., Nishimasu, H., Nureki, O., & Toki, S. (2019). Genome editing in plants by engineered CRISPR-Cas9 recognizing NG PAM. Nature Plants, 5(1), 14-17. https://doi.org/10.1038/s41477-018-0321-8
Fu, Y., Foden, J. A., Khayter, C., Maeder, M. L., Reyon, D., Joung, J. K., & Sander, J. D. (2013). High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nature Biotechnology, 31(9), 822-826. https://doi.org/10.1038/nbt.2623
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. (2016). Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology, 17(1), 148. https://doi.org/10.1186/s13059-016-1012-2
Heigwer, F., Kerr, G., & Boutros, M. (2014). E-CRISP: fast CRISPR target site identification. Nature Methods, 11(2), 122-123. https://doi.org/10.1038/nmeth.2812
Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S., Agarwala, V., Li, Y., Fine, E. J., Wu, X., Shalem, O., Cradick, T. J., Marraffini, L. A., Bao, G., & Zhang, F. (2013). DNA targeting specificity of RNA-guided Cas9 nucleases. Nature Biotechnology, 31(9), 827-832. https://doi.org/10.1038/nbt.2647
Japan embraces CRISPR-edited fish. (2022). Nature Biotechnology, 40(1), 10-10. https://doi.org/10.1038/s41587-021-01197-8
Kim, D., Bae, S., Park, J., Kim, E., Kim, S., Yu, H. R., Hwang, J., Kim, J.-I., & Kim, J.-S. (2015). Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nature Methods, 12(3), 237-243. https://doi.org/10.1038/nmeth.3284
Kim, D., & Kim, J. S. (2018). DIG-seq: a genome-wide CRISPR off-target profiling method using chromatin DNA. Genome Research, 28(12), 1894-1900. https://doi.org/10.1101/gr.236620.118
Kim, D. Y., Moon, S. B., Ko, J.-H., Kim, Y.-S., & Kim, D. (2020). Unbiased investigation of specificities of prime editing systems in human cells. Nucleic Acids Research, 48(18), 10576-10589. https://doi.org/10.1093/nar/gkaa764
Kleinstiver, B. P., Pattanayak, V., Prew, M. S., Tsai, S. Q., Nguyen, N. T., Zheng, Z., & Joung, J. K. (2016). High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature, 529(7587), 490-495. https://doi.org/10.1038/nature16526
Leinonen, R., Sugawara, H., Shumway, M., & International Nucleotide Sequence Database Collaboration. (2011). The Sequence Read Archive. Nucleic Acids Research, 39(Database issue), D19-D21. https://doi.org/10.1093/nar/gkq1019
Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv.
Lin, J., & Wong, K. C. (2018). Off-target predictions in CRISPR-Cas9 gene editing using deep learning. Bioinformatics, 34(17), i656-i663. https://doi.org/10.1093/bioinformatics/bty554
Mendes, R. D., Sarmento, L. M., Canté-Barrett, K., Zuurbier, L., Buijs-Gladdines, J. G., Póvoa, V., Smits, W. K., Abecasis, M., Yunes, J. A., Sonneveld, E., Horstmann, M. A., Pieters, R., Barata, J. T., & Meijerink, J. P. (2014). PTEN microdeletions in T-cell acute lymphoblastic leukemia are caused by illegitimate RAG-mediated recombination events. Blood, 124(4), 567-578. https://doi.org/10.1182/blood-2014-03-562751
Modrzejewski, D., Hartung, F., Lehnert, H., Sprink, T., Kohl, C., Keilwagen, J., & Wilhelm, R. (2020). Which factors affect the occurrence of off-target effects caused by the use of CRISPR/Cas: a systematic review in plants. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.574959
Myers, E. W., & Miller, W. (1988). Optimal alignments in linear space. Bioinformatics, 4(1), 11-17.
Papaemmanuil, E., Rapado, I., Li, Y., Potter, N. E., Wedge, D. C., Tubio, J., Alexandrov, L. B., Van Loo, P., Cooke, S. L., Marshall, J., Martincorena, I., Hinton, J., Gundem, G., van Delft, F. W., Nik-Zainal, S., Jones, D. R., Ramakrishna, M., Titley, I., Stebbings, L., . . . Campbell, P. J. (2014). RAG-mediated recombination is the predominant driver of oncogenic rearrangement in ETV6-RUNX1 acute lymphoblastic leukemia. Nature Genetics, 46(2), 116-125. https://doi.org/10.1038/ng.2874
Peng, H., Zheng, Y., Zhao, Z., Liu, T., & Li, J. (2018). Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions. Bioinformatics, 34(17), i757-i765. https://doi.org/10.1093/bioinformatics/bty558
Picard toolkit. (2018). In: Broad Institute, GitHub repository.
Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013). Genome engineering using the CRISPR-Cas9 system. Nature Protocols, 8(11), 2281-2308. https://doi.org/10.1038/nprot.2013.143
Shan, Q., Wang, Y., Li, J., Zhang, Y., Chen, K., Liang, Z., Zhang, K., Liu, J., Xi, J. J., Qiu, J.-L., & Gao, C. (2013). Targeted genome modification of crop plants using a CRISPR-Cas system. Nature Biotechnology, 31(8), 686-688. https://doi.org/10.1038/nbt.2650
Slaymaker, I. M., Gao, L., Zetsche, B., Scott, D. A., Yan, W. X., & Zhang, F. (2016). Rationally engineered Cas9 nucleases with improved specificity. Science, 351(6268), 84-88. https://doi.org/doi:10.1126/science.aad5227
Stemmer, M., Thumberger, T., del Sol Keyer, M., Wittbrodt, J., & Mateo, J. L. (2015). CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLOS ONE, 10(4), e0124633. https://doi.org/10.1371/journal.pone.0124633
Tan, Y., Chu, A. H. Y., Bao, S., Hoang, D. A., Kebede, F. T., Xiong, W., Ji, M., Shi, J., & Zheng, Z. (2019). Rationally engineered Staphylococcus aureus Cas9 nucleases with high genome-wide specificity. Proceedings of the National Academy of Sciences, 116(42), 20969-20976. https://doi.org/doi:10.1073/pnas.1906843116
Tang, J., Chen, L., & Liu, Y.-G. (2019). Off-target effects and the solution. Nature Plants, 5(4), 341-342. https://doi.org/10.1038/s41477-019-0406-z
Tsai, S. Q., Zheng, Z., Nguyen, N. T., Liebers, M., Topkar, V. V., Thapar, V., Wyvekens, N., Khayter, C., Iafrate, A. J., Le, L. P., Aryee, M. J., & Joung, J. K. (2015). GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nature Biotechnology, 33(2), 187-197. https://doi.org/10.1038/nbt.3117
Waltz, E. (2022). GABA-enriched tomato is first CRISPR-edited food to enter market. Nature Biotechnology, 40(1), 9-11. https://doi.org/10.1038/d41587-021-00026-2
Xu, Y., & Li, Z. (2020). CRISPR-Cas systems: Overview, innovations and applications in human disease research and gene therapy. Computational and Structural Biotechnology Journal, 18, 2401-2415. https://doi.org/10.1016/j.csbj.2020.08.031
Zhang, X.-H., Tee, L. Y., Wang, X.-G., Huang, Q.-S., & Yang, S.-H. (2015). Off-target Effects in CRISPR/Cas9-mediated Genome Engineering. Molecular Therapy - Nucleic Acids, 4, e264. https://doi.org/https://doi.org/10.1038/mtna.2015.37
Zhou, J., Chen, P., Wang, H., Liu, H., Li, Y., Zhang, Y., Wu, Y., Paek, C., Sun, Z., Lei, J., & Yin, L. (2022). Cas12a variants designed for lower genome-wide off-target effect through stringent PAM recognition. Molecular Therapy, 30(1), 244-255. https://doi.org/https://doi.org/10.1016/j.ymthe.2021.10.010
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88732-
dc.description.abstractnonezh_TW
dc.description.abstractOff-target mutations are one of the major concerns raised about genome editing nucle-ases, and many efforts have been made to predict them. However, the accuracy of the predictions remains unsatisfactory possibly because our knowledge of mutation pat-terns is insufficient. In this way, although this technique is already at the application stage, the basic characteristics of off-target mutations are still to be investigated. Therefore, the objective of this research is to elucidate the patterns of off-target muta-tions reported in multiple studies that utilized in vivo GUIDE-seq and in vitro Dige-nome-seq methods. The results showed that digested sites were identical or highly sim-ilar to each other in most of the cases, while they sometimes varied considerably if dif-ferent enzymes are used; 16 insignificant and 207 significant cases were found in GUIDE-seq datasets. A comparison among three independent studies for a same en-zyme and target site showed that the digested sequences patterns were similar in all eight cases. In addition, a comparative analysis between experiment-based GUIDE-seq and in silico CRISPOR methods revealed limitations in predicting off-target mutations, particularly for SpCas9 variants and alternative enzymes. While CRISPOR has shown some success in identifying off-target sequences for the WT SpCas9 enzyme, it still generates a notable number of false positives. To conclude, off-target mutations might not be really predictable, and are determined mainly by the intrinsic nature of an en-zyme, and if new variants of an enzyme is engineered, its characteristics should be re-investigated. Furthermore, we encountered problem when analyzing the Digenome-seq datasets, while Arabidopsis data could be analyzed successfully, the methodology should be further improved to analyze the human datasets.en
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dc.description.tableofcontentsMaster’s thesis acceptance certificate i
ACKNOWLEDGMENT ii
ABSTRACT iii
Table of Contents iv
List of Tables vii
List of Figures viii
Abbreviation x
Chapter 1. Introduction 1
1.1. Genome Editing 1
1.2. Off-target mutations 2
1.3. Factors affecting off-target mutations 3
1.4. Off-target identification methods 4
1.5. Research objective 9
Chapter 2. Materials and Methods 10
2.1. Materials 10
2.1.1. GUIDE-seq datasets 10
2.1.2. Digenome-seq datasets 12
2.2. Methods 14
2.2.1. Workflow of GUIDE-seq analysis 14
2.2.2. GUIDE-seq preprocessing 14
2.2.3. GUIDE-seq off-target sequences identification 15
2.2.4. Workflow of Digenome-seq analysis 16
2.2.5. Digenome-seq preprocessing 16
2.2.6. Digenome-seq DSBs position identification 17
2.2.7. Digenome-seq off-target sequences identification 17
2.2.8. Off-target mutation patterns 18
2.2.9. Additional analysis of Digenome-seq datasets 19
2.2.10. CRISPOR: off-target prediction method 19
Chapter 3. Results 21
3.1. GUIDE-seq analysis 21
3.1.1. Identification of digested sequences 21
3.1.2. Analysis of digested sequence mutation patterns 24
3.2. Digenome-seq analysis 32
3.2.1. Identification of digested sequences 32
3.2.2. Analysis of off-target sequences mutation patterns 36
3.2.3. Additional analysis 37
3.3. Comparison between results from prediction method and actual data 41
Chapter 4. Discussions 48
4.1. Mutation patterns of off-target sequences 48
4.1.1. GUIDE-seq datasets 48
4.1.2. Digenome-seq – Arabidopsis datasets 50
4.2. Two programs used to identify Digenome-seq DSB sites 51
4.3. Additional analysis of human datasets 52
4.4. Comparison between predicted off-target sequences and actual data 52
4.5. Limitations of this study 55
4.5.1. Limited datasets from public database 55
4.5.2. Limitation in the statistical program to identify Digenome-seq off-target sequences of human datasets 56
Chapter 5. Conclusion and Perspective 58
References 60
Supplementary Data 67
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dc.language.isoen-
dc.subject基因組編輯zh_TW
dc.subject脫靶效應zh_TW
dc.subjectCRISPR-Caszh_TW
dc.subjectoff-target mutationsen
dc.subjectgenome editingen
dc.subjectCRISPR-Casen
dc.titleComprehensive Genome Analysis to Elucidate CRISPR-Cas Off-Target Mutation Patterns on the Basis of in vivo, in vitro, and in silico Experimentszh_TW
dc.titleComprehensive Genome Analysis to Elucidate CRISPR-Cas Off-Target Mutation Patterns on the Basis of in vivo, in vitro, and in silico Experimentsen
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee洪傳揚;蔡育彰zh_TW
dc.contributor.oralexamcommitteeChwan-Yang Hong;Yu-Chang Tsaien
dc.subject.keyword基因組編輯,CRISPR-Cas,脫靶效應,zh_TW
dc.subject.keywordCRISPR-Cas,off-target mutations,genome editing,en
dc.relation.page72-
dc.identifier.doi10.6342/NTU202302071-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2023-08-08-
dc.contributor.author-college國際學院-
dc.contributor.author-dept全球農業科技與基因體科學碩士學位學程-
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