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Cited by 861

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Highly Efficient Targeted Mutagenesis of Drosophila with the CRISPR/Cas9 System

Open access
Bassett, Andrew R., Tibbit, Charlotte, Ponting, Chris P., Liu, Ji-Long·2013·Cell Reports

Abstract

Here, we present a simple and highly efficient method for generating and detecting mutations of any gene in Drosophila melanogaster through the use of the CRISPR/Cas9 system (clustered regularly interspaced palindromic repeats/CRISPR-associated). We show that injection of RNA into the Drosophila embryo can induce highly efficient mutagenesis of desired target genes in up to 88% of injected flies. These mutations can be transmitted through the germline to make stable lines. Our system provides at least a 10-fold improvement in efficiency over previously published reports, enabling wider application of this technique. We also describe a simple and highly sensitive method of detecting mutations in the target gene by high-resolution melt analysis and discuss how the new technology enables the study of gene function.

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Crossref · 10.1016/j.celrep.2013.06.020

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Metadata

Authors
Bassett, A. R., Tibbit, C., Ponting, C. P., & Liu, J.-L.
Venue
Cell Reports
Publisher
Elsevier BV
Published
2013-07
Volume
4
Issue
1
Pages
220-228
ISSN
2211-1247 (ISSN-L)
OpenAlex
W2122941722
Cited by
861
References
9
FWCI
36.18
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Highly Efficient Targeted Mutagenesis of Drosophila with the CRISPR/Cas9 System. https://app.cochat.ai/papers/10.1016/j.celrep.2013.06.020
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Bassett, A. R., Tibbit, C., Ponting, C. P., & Liu, J.-L. (2013). Highly Efficient Targeted Mutagenesis of Drosophila with the CRISPR/Cas9 System. Cell Reports. https://doi.org/10.1016/j.celrep.2013.06.020

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.