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Nucleic Acids Res. 2015 Oct 15;43(18):e118. doi: 10.1093/nar/gkv575. Epub 2015 Jun 1.

Cas9-chromatin binding information enables more accurate CRISPR off-target prediction.

Author information

1
University of Virginia, School of Medicine, Department of Biochemistry and Molecular Genetics, 1340 Jefferson Park Ave, Jordan Hall, Room: 6240, Charlottesville, VA 22903, USA University of Virginia, Center for Public Health Genomics, Charlottesville VA 22903, USA.
2
University of Virginia, School of Medicine, Department of Biochemistry and Molecular Genetics, 1340 Jefferson Park Ave, Jordan Hall, Room: 6240, Charlottesville, VA 22903, USA.
3
University of Virginia, School of Medicine, Department of Biochemistry and Molecular Genetics, 1340 Jefferson Park Ave, Jordan Hall, Room: 6240, Charlottesville, VA 22903, USA University of Virginia, Department of Computer Science, Charlottesville VA 22903, USA University of Virginia, Department of Public Health Sciences, Charlottesville VA 22903, USA.
4
University of Virginia, Center for Public Health Genomics, Charlottesville VA 22903, USA.
5
University of Virginia, School of Medicine, Department of Biochemistry and Molecular Genetics, 1340 Jefferson Park Ave, Jordan Hall, Room: 6240, Charlottesville, VA 22903, USA adli@virginia.edu.

Abstract

The CRISPR system has become a powerful biological tool with a wide range of applications. However, improving targeting specificity and accurately predicting potential off-targets remains a significant goal. Here, we introduce a web-based CR: ISPR/Cas9 O: ff-target P: rediction and I: dentification T: ool (CROP-IT) that performs improved off-target binding and cleavage site predictions. Unlike existing prediction programs that solely use DNA sequence information; CROP-IT integrates whole genome level biological information from existing Cas9 binding and cleavage data sets. Utilizing whole-genome chromatin state information from 125 human cell types further enhances its computational prediction power. Comparative analyses on experimentally validated datasets show that CROP-IT outperforms existing computational algorithms in predicting both Cas9 binding as well as cleavage sites. With a user-friendly web-interface, CROP-IT outputs scored and ranked list of potential off-targets that enables improved guide RNA design and more accurate prediction of Cas9 binding or cleavage sites.

PMID:
26032770
PMCID:
PMC4605288
DOI:
10.1093/nar/gkv575
[Indexed for MEDLINE]
Free PMC Article

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