Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation


Components of the prokaryotic clustered, regularly interspaced, short palindromic repeats (CRISPR) loci have recently been repurposed for use in mammalian cells1,2,3,4,5,6. The CRISPR-associated (Cas)9 can be programmed with a single guide RNA (sgRNA) to generate site-specific DNA breaks, but there are few known rules governing on-target efficacy of this system7,8. We created a pool of sgRNAs, tiling across all possible target sites of a panel of six endogenous mouse and three endogenous human genes and quantitatively assessed their ability to produce null alleles of their target gene by antibody staining and flow cytometry. We discovered sequence features that improved activity, including a further optimization of the protospacer-adjacent motif (PAM) of Streptococcus pyogenes Cas9. The results from 1,841 sgRNAs were used to construct a predictive model of sgRNA activity to improve sgRNA design for gene editing and genetic screens. We provide an online tool for the design of highly active sgRNAs for any gene of interest.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: sgRNA activity screens in mouse and human cells.
Figure 2: Features of sgRNA activity.
Figure 3: Model of sgRNA activity.

Accession codes

Primary accessions

Sequence Read Archive


  1. 1

    Barrangou, R. et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709–1712 (2007).

    CAS  Article  Google Scholar 

  2. 2

    Garneau, J.E. et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

    CAS  Article  Google Scholar 

  3. 3

    Sapranauskas, R. et al. The Streptococcus thermophilus CRISPR/Cas system provides immunity in Escherichia coli. Nucleic Acids Res. 39, 9275–9282 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    Article  Google Scholar 

  5. 5

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  Article  Google Scholar 

  7. 7

    Wang, T., Wei, J.J., Sabatini, D.M. & Lander, E.S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Gagnon, J.A. et al. Efficient mutagenesis by Cas9 protein-mediated oligonucleotide insertion and large-scale assessment of single-guide RNAs. PLoS ONE 9, e98186 (2014).

    Article  Google Scholar 

  9. 9

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS  Article  Google Scholar 

  10. 10

    Koike-Yusa, H., Li, Y., Tan, E.-P., Del Castillo Velasco-Herrera, M. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

    CAS  Article  Google Scholar 

  11. 11

    Zhou, Y. et al. High-throughput screening of a CRISPR-Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl. Acad. Sci. USA 105, 20380–20385 (2008).

    CAS  Article  Google Scholar 

  13. 13

    Cheung, H.W. et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl. Acad. Sci. USA 108, 12372–12377 (2011).

    CAS  Article  Google Scholar 

  14. 14

    Hsu, P.D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

    CAS  Article  Google Scholar 

  15. 15

    Cho, S.W. et al. Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases. Genome Res. 24, 132–141 (2014).

    CAS  Article  Google Scholar 

  16. 16

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    CAS  Article  Google Scholar 

  17. 17

    Yang, H. et al. One-step generation of mice carrying reporter and conditional alleles by CRISPR/Cas-mediated genome engineering. Cell 154, 1370–1379 (2013).

    CAS  Article  Google Scholar 

  18. 18

    Fu, Y., Sander, J.D., Reyon, D., Cascio, V.M. & Joung, J.K. Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279–284 (2014).

    CAS  Article  Google Scholar 

  19. 19

    Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

    CAS  Article  Google Scholar 

  20. 20

    Wu, X. et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nat. Biotechnol. 32, 670–676 (2014).

    CAS  Article  Google Scholar 

  21. 21

    Kuscu, C., Arslan, S., Singh, R., Thorpe, J. & Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 32, 677–683 (2014).

    CAS  Article  Google Scholar 

  22. 22

    Reynolds, A. et al. Rational siRNA design for RNA interference. Nat. Biotechnol. 22, 326–330 (2004).

    CAS  Article  Google Scholar 

  23. 23

    Fellmann, C. et al. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol. Cell 41, 733–746 (2011).

    CAS  Article  Google Scholar 

  24. 24

    Jiang, W., Bikard, D., Cox, D., Zhang, F. & Marraffini, L.A. RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Nat. Biotechnol. 31, 233–239 (2013).

    CAS  Article  Google Scholar 

  25. 25

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  Google Scholar 

Download references


We thank M. Waring (Ragon Institute, Cambridge, MA) for expert assistance with flow cytometry; S. Rosenbluh (Broad Institute) for sharing the pLX_311-Cas9 vector; T. Mason (Broad Institute) for Illumina sequencing advice and execution; D. Alan, A. Brown, M. Tomko, M. Greene and T. Green (Broad Institute) for assistance in building the sgRNA design website; J. Listgarten and N. Fuso (Microsoft Research) for a critical analysis of the sgRNA activity prediction model.

Author information




E.H., D.B.G., Z.T. and J.G.D. designed experiments; E.H., M.S., D.B.G. and Z.T. ran experiments; E.H. and J.G.D. analyzed experimental results; M.H. and J.G.D. analyzed sequencing data and developed analysis tools; I.S. developed the sgRNA scoring model; E.H., D.E.R. and J.G.D. wrote the manuscript with help from other authors; B.L.E., R.J.X. and D.E.R. supervised the research. J.G.D. is a Merkin Institute Fellow. Z.T. is supported by US National Institutes of Health 5T32CA009172-39.

Corresponding authors

Correspondence to John G Doench or David E Root.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 4–6 and 9 (PDF 4229 kb)

Supplementary Table 1

List of 6500 synthesized oligonucleotides. A small number of oligonucleotides appear more than once. (XLSX 123 kb)

Supplementary Table 2

Full dataset of Illumina sequencing reads from the mouse pool. Each sample was tagged with several different sample barcodes and thus appears in multiple columns of data. See Methods for additional details on data analysis. (XLSX 2375 kb)

Supplementary Table 3

Full dataset of Illumina sequencing reads from the human pool. (XLSX 547 kb)

Supplementary Table 7

List of 1,841 CDS-targeting sgRNAs. On-target activity is expressed as within-gene percent-rank in the marker-negative population relative to the unsorted population. The predicted sgRNA score from the final model is also given. See Methods for additional details on data analysis. (XLSX 204 kb)

Supplementary Table 8

For each single and dinucleotide feature, number of sgRNA examples, frequency of scoring in the top quintile percent-rank for that target gene, and statistical significance for over- or under-representation of sgRNAs with that feature in the most-active sgRNA quintile. (XLSX 102 kb)

Supplementary Table 10

1,278 sgRNAs targeting 414 essential genes in A375 cells. sgRNA activity is expressed as log2 fold change in abundance during two weeks of growth. See Methods for additional details on data analysis. (XLSX 252 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Doench, J., Hartenian, E., Graham, D. et al. Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nat Biotechnol 32, 1262–1267 (2014). https://doi.org/10.1038/nbt.3026

Download citation

Further reading


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing