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A systematic evaluation of nucleotide properties for CRISPR sgRNA design
BACKGROUND: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. RESULTS: By borrowing knowledge from oligonucleotide desi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461693/ https://www.ncbi.nlm.nih.gov/pubmed/28587596 http://dx.doi.org/10.1186/s12859-017-1697-6 |
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author | Kuan, Pei Fen Powers, Scott He, Shuyao Li, Kaiqiao Zhao, Xiaoyu Huang, Bo |
author_facet | Kuan, Pei Fen Powers, Scott He, Shuyao Li, Kaiqiao Zhao, Xiaoyu Huang, Bo |
author_sort | Kuan, Pei Fen |
collection | PubMed |
description | BACKGROUND: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. RESULTS: By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). CONCLUSION: Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1697-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5461693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54616932017-06-07 A systematic evaluation of nucleotide properties for CRISPR sgRNA design Kuan, Pei Fen Powers, Scott He, Shuyao Li, Kaiqiao Zhao, Xiaoyu Huang, Bo BMC Bioinformatics Research Article BACKGROUND: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. RESULTS: By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). CONCLUSION: Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1697-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-06 /pmc/articles/PMC5461693/ /pubmed/28587596 http://dx.doi.org/10.1186/s12859-017-1697-6 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Kuan, Pei Fen Powers, Scott He, Shuyao Li, Kaiqiao Zhao, Xiaoyu Huang, Bo A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title | A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title_full | A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title_fullStr | A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title_full_unstemmed | A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title_short | A systematic evaluation of nucleotide properties for CRISPR sgRNA design |
title_sort | systematic evaluation of nucleotide properties for crispr sgrna design |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461693/ https://www.ncbi.nlm.nih.gov/pubmed/28587596 http://dx.doi.org/10.1186/s12859-017-1697-6 |
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