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Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering
CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been developed for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602897/ https://www.ncbi.nlm.nih.gov/pubmed/37738140 http://dx.doi.org/10.1093/nar/gkad736 |
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author | Noshay, Jaclyn M Walker, Tyler Alexander, William G Klingeman, Dawn M Romero, Jonathon Walker, Angelica M Prates, Erica Eckert, Carrie Irle, Stephan Kainer, David Jacobson, Daniel A |
author_facet | Noshay, Jaclyn M Walker, Tyler Alexander, William G Klingeman, Dawn M Romero, Jonathon Walker, Angelica M Prates, Erica Eckert, Carrie Irle, Stephan Kainer, David Jacobson, Daniel A |
author_sort | Noshay, Jaclyn M |
collection | PubMed |
description | CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been developed for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and new public resource, produced with quantum chemical tensors, for interpreting and predicting sgRNA efficiency. Feature engineering for sgRNA efficiency is performed using an explainable-artificial intelligence model: iterative Random Forest (iRF). By encoding quantitative attributes of position-specific sequences for Escherichia coli sgRNAs, we identify important traits for sgRNA design in bacterial species. Additionally, we show that expanding positional encoding to quantum descriptors of base-pair, dimer, trimer, and tetramer sequences captures intricate interactions in local and neighboring nucleotides of the target DNA. These features highlight variation in CRISPR-Cas9 sgRNA dynamics between E. coli and H. sapiens genomes. These novel encodings of sgRNAs enhance our understanding of the elaborate quantum biological processes involved in CRISPR-Cas9 machinery. |
format | Online Article Text |
id | pubmed-10602897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106028972023-10-28 Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering Noshay, Jaclyn M Walker, Tyler Alexander, William G Klingeman, Dawn M Romero, Jonathon Walker, Angelica M Prates, Erica Eckert, Carrie Irle, Stephan Kainer, David Jacobson, Daniel A Nucleic Acids Res Computational Biology CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been developed for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and new public resource, produced with quantum chemical tensors, for interpreting and predicting sgRNA efficiency. Feature engineering for sgRNA efficiency is performed using an explainable-artificial intelligence model: iterative Random Forest (iRF). By encoding quantitative attributes of position-specific sequences for Escherichia coli sgRNAs, we identify important traits for sgRNA design in bacterial species. Additionally, we show that expanding positional encoding to quantum descriptors of base-pair, dimer, trimer, and tetramer sequences captures intricate interactions in local and neighboring nucleotides of the target DNA. These features highlight variation in CRISPR-Cas9 sgRNA dynamics between E. coli and H. sapiens genomes. These novel encodings of sgRNAs enhance our understanding of the elaborate quantum biological processes involved in CRISPR-Cas9 machinery. Oxford University Press 2023-09-20 /pmc/articles/PMC10602897/ /pubmed/37738140 http://dx.doi.org/10.1093/nar/gkad736 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Noshay, Jaclyn M Walker, Tyler Alexander, William G Klingeman, Dawn M Romero, Jonathon Walker, Angelica M Prates, Erica Eckert, Carrie Irle, Stephan Kainer, David Jacobson, Daniel A Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title | Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title_full | Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title_fullStr | Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title_full_unstemmed | Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title_short | Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering |
title_sort | quantum biological insights into crispr-cas9 sgrna efficiency from explainable-ai driven feature engineering |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602897/ https://www.ncbi.nlm.nih.gov/pubmed/37738140 http://dx.doi.org/10.1093/nar/gkad736 |
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