Computational Prediction of CRISPR/Cas9 Target Sites Reveals Potential Off-Target Risks in Human and Mouse

  • Qingbo Wang
  • Kumiko Ui-TeiEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1630)


The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas) system is a prominent genome engineering technology. In the CRISPR/Cas system, the RNA-guided endonuclease Cas protein introduces a DNA double-stranded break at the genome position recognized by a guide RNA (gRNA) based on complementary base-pairing of about 20-nucleotides in length. The 8- or 12-mer gRNA sequence in the proximal region is especially important for target recognition, and the genes with sequence complementarity to such regions are often disrupted. To carry out target site-specific genome editing, we released the CRISPRdirect ( website. This website allows us to select target site-specific gRNA sequences by performing exhaustive searches against entire genomic sequences. In this study, target site-specific gRNA sequences were designed for human, mouse, Drosophila melanogaster, and Caenorhabditis elegans. The calculation results revealed that at least five gRNA sequences, each of them having only one perfectly complementary site in the whole genome, could be designed for more than 95% of genes, regardless of the organism. Next, among those gRNAs, we selected gRNAs that did not have any other complementary site to the unique 12-mer proximal sequences to avoid possible off-target effects. This computational prediction revealed that target site-specific gRNAs are selectable for the majority of genes in D. melanogaster and C. elegans. However, for >50% of genes in humans and mice, there are no target sites without possible off-target effects.

Key words

CRISPR/Cas9 Target site Off-target gene CRISPR direct 



We thank Dr. Yuki Naito for valuable discussion and technical advice. The English in this document has been checked by at least two professional editors, both native speakers of English. This work was supported by the grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan to K.U.-T.


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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Bioinformatics and Systems Biology, Faculty of ScienceThe University of TokyoTokyoJapan
  2. 2.Department of Biological SciencesGraduate School of Science, The University of TokyoTokyoJapan
  3. 3.Department of Computational BiologyGraduate School of Frontier Sciences, The University of TokyoChibaJapan

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