Deep learning and CRISPR-Cas13D ortholog discovery for optimized RNA targeting
Abstract/Contents
- Abstract
- Effective 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. 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2024; ©2024 |
| Publication date | 2024; 2024 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wei, Jingyi, (Bioengineer) |
|---|---|
| Degree supervisor | Konermann, Silvana |
| Thesis advisor | Konermann, Silvana |
| Thesis advisor | Bintu, Lacramioara |
| Thesis advisor | Chang, Howard Y. (Howard Yuan-Hao), 1972- |
| Thesis advisor | Quake, Stephen Ronald |
| Degree committee member | Bintu, Lacramioara |
| Degree committee member | Chang, Howard Y. (Howard Yuan-Hao), 1972- |
| Degree committee member | Quake, Stephen Ronald |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jingyi Wei. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2024. |
| Location | https://purl.stanford.edu/cw868kk0098 |
Access conditions
- Copyright
- © 2024 by Jingyi Wei
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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