2026-09-20T04:38:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1274772026-06-06T01:03:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Gifford.
Louie, Wilson,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-09-15T21:59:45Z
2020-09-15T21:59:45Z
2020
2020
https://hdl.handle.net/1721.1/127477
1193019939
Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 83-86).
The restoration of gene function by the induced skipping of a deleterious exon has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that offers permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.72 and 0.91, when using threshold predicted exon skipping frequencies of 50% and 70% respectively. We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.
by Wilson Louie.
M.Eng. in Computer Science and Molecular Biology
M.Eng.inComputerScienceandMolecularBiology Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
86 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Machine learning based CRISPR gRNA design for therapeutic exon skipping
Machine learning based Clustered regularly interspaced short palindromic repeats gRNA design for therapeutic exon skipping
Thesis
application/pdf
2020-09-15T21:59:44Z
Publication
Master
EECS
unknown
unknown
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<Abstract>The restoration of gene function by the induced skipping of a deleterious exon has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that offers permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.72 and 0.91, when using threshold predicted exon skipping frequencies of 50% and 70% respectively. We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.</Abstract>
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