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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 伊藤剛 | zh_TW |
| dc.contributor.advisor | Takeshi Itoh | en |
| dc.contributor.author | 翁明蓮 | zh_TW |
| dc.contributor.author | Celine Kurniawan | en |
| dc.date.accessioned | 2023-08-15T17:33:40Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-07 | - |
| dc.identifier.citation | Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. In: Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88732 | - |
| dc.description.abstract | none | zh_TW |
| dc.description.abstract | Off-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 |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:33:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:33:40Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Master’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 | - |
| dc.language.iso | en | - |
| dc.title | Comprehensive Genome Analysis to Elucidate CRISPR-Cas Off-Target Mutation Patterns on the Basis of in vivo, in vitro, and in silico Experiments | zh_TW |
| dc.title | Comprehensive Genome Analysis to Elucidate CRISPR-Cas Off-Target Mutation Patterns on the Basis of in vivo, in vitro, and in silico Experiments | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪傳揚;蔡育彰 | zh_TW |
| dc.contributor.oralexamcommittee | Chwan-Yang Hong;Yu-Chang Tsai | en |
| dc.subject.keyword | 基因組編輯,CRISPR-Cas,脫靶效應, | zh_TW |
| dc.subject.keyword | CRISPR-Cas,off-target mutations,genome editing, | en |
| dc.relation.page | 72 | - |
| dc.identifier.doi | 10.6342/NTU202302071 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-08 | - |
| 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 | - |
| Appears in Collections: | 全球農業科技與基因體科學碩士學位學程 | |
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