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Deep Learning and CRISPR-Cas13d Ortholog Discovery for Optimized RNA Targeting.
Deep Learning and CRISPR-Cas13d Ortholog Discovery for Optimized RNA Targeting.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017161480
- International Standard Book Number
- 9798382235493
- Dewey Decimal Classification Number
- 575
- Main Entry-Personal Name
- Jingyi Wei.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Stanford University., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 95 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- General Note
- Advisor: Silvana Konermann.
- Dissertation Note
- Thesis (Ph.D.)--Stanford University, 2024.
- Summary, Etc.
- 요약Effective mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and computational models for prediction of high efficiency guides. Here, we quantified the performance of 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm (https://www.RNAtargeting.org). I further validated the model across multiple genes and human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides, elucidating CasRx targeting preferences and mechanisms.
- Subject Added Entry-Topical Term
- Genetics.
- Subject Added Entry-Topical Term
- Bioengineering.
- Index Term-Uncontrolled
- RNA therapeutics
- Index Term-Uncontrolled
- Biological discovery
- Index Term-Uncontrolled
- CRISPR-Cas13 ribonucleases
- Added Entry-Corporate Name
- Stanford University.
- Host Item Entry
- Dissertations Abstracts International. 85-11B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:655464
MARC
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■1001 ▼aJingyi Wei.
■24510▼aDeep Learning and CRISPR-Cas13d Ortholog Discovery for Optimized RNA Targeting.
■260 ▼a[S.l.]▼bStanford University. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a95 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Silvana Konermann.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aEffective mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and computational models for prediction of high efficiency guides. Here, we quantified the performance of 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm (https://www.RNAtargeting.org). I further validated the model across multiple genes and human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides, elucidating CasRx targeting preferences and mechanisms.
■590 ▼aSchool code: 0212.
■650 4▼aGenetics.
■650 4▼aBioengineering.
■653 ▼aRNA therapeutics
■653 ▼aBiological discovery
■653 ▼aCRISPR-Cas13 ribonucleases
■690 ▼a0202
■690 ▼a0369
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161480▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
