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Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework

As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments hav...

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Detalles Bibliográficos
Autores principales: Liu, Xiuqin, Wang, Shuya, Ai, Dongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180579/
https://www.ncbi.nlm.nih.gov/pubmed/35681543
http://dx.doi.org/10.3390/cells11111847
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author Liu, Xiuqin
Wang, Shuya
Ai, Dongmei
author_facet Liu, Xiuqin
Wang, Shuya
Ai, Dongmei
author_sort Liu, Xiuqin
collection PubMed
description As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments have demonstrated that CRISPR/Cas9-generated cellular-repair outcomes depend on local sequence features. Therefore, the repair outcomes after DNA break can be predicted by sequences near the cleavage sites. However, existing prediction methods rely on manually constructed features or insufficiently detailed prediction labels. They cannot satisfy clinical-level-prediction accuracy, which limit the performance of these models to existing knowledge about CRISPR/Cas9 editing. We predict 557 repair labels of DNA, covering the vast majority of Cas9-generated mutational outcomes, and build a deep learning model called Apindel, to predict CRISPR/Cas9 editing outcomes. Apindel, automatically, trains the sequence features of DNA with the GloVe model, introduces location information through Positional Encoding (PE), and embeds the trained-word vector matrixes into a deep learning model, containing BiLSTM and the Attention mechanism. Apindel has better performance and more detailed prediction categories than the most advanced DNA-mutation-predicting models. It, also, reveals that nucleotides at different positions relative to the cleavage sites have different influences on CRISPR/Cas9 editing outcomes.
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spelling pubmed-91805792022-06-10 Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework Liu, Xiuqin Wang, Shuya Ai, Dongmei Cells Article As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments have demonstrated that CRISPR/Cas9-generated cellular-repair outcomes depend on local sequence features. Therefore, the repair outcomes after DNA break can be predicted by sequences near the cleavage sites. However, existing prediction methods rely on manually constructed features or insufficiently detailed prediction labels. They cannot satisfy clinical-level-prediction accuracy, which limit the performance of these models to existing knowledge about CRISPR/Cas9 editing. We predict 557 repair labels of DNA, covering the vast majority of Cas9-generated mutational outcomes, and build a deep learning model called Apindel, to predict CRISPR/Cas9 editing outcomes. Apindel, automatically, trains the sequence features of DNA with the GloVe model, introduces location information through Positional Encoding (PE), and embeds the trained-word vector matrixes into a deep learning model, containing BiLSTM and the Attention mechanism. Apindel has better performance and more detailed prediction categories than the most advanced DNA-mutation-predicting models. It, also, reveals that nucleotides at different positions relative to the cleavage sites have different influences on CRISPR/Cas9 editing outcomes. MDPI 2022-06-05 /pmc/articles/PMC9180579/ /pubmed/35681543 http://dx.doi.org/10.3390/cells11111847 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xiuqin
Wang, Shuya
Ai, Dongmei
Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title_full Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title_fullStr Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title_full_unstemmed Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title_short Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework
title_sort predicting crispr/cas9 repair outcomes by attention-based deep learning framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180579/
https://www.ncbi.nlm.nih.gov/pubmed/35681543
http://dx.doi.org/10.3390/cells11111847
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