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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9180579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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