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GUIDES: sgRNA design for loss-of-function screens

Nature Methods volume 14, pages 831–832 (2017)Cite this article

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Genome-scale CRISPR–Cas9 knockout libraries have emerged as powerful tools for unbiased phenotypic screens1. These libraries contain a fixed number of Cas9 single-guide RNAs (sgRNAs) targeting each gene in the genome and typically require large numbers of cells (>108) to maintain genome-scale representation. However, there are many applications for which it would be preferable to design a custom library targeting specific gene sets (for example, kinases, transcription factors, chromatin modifiers, the druggable genome) with higher coverage for these specific genes. To address this need, we developed Graphical User Interface for DNA Editing Screens (GUIDES), a web application that designs CRISPR knockout libraries to target custom subsets of genes in the human or mouse genome (available at http://guides.sanjanalab.org/ and https://github.com/sanjanalab/GUIDES).

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Figure 1: GUIDES design environment.

References

  1. Shalem, O., Sanjana, N.E. & Zhang, F. Nat. Rev. Genet. 16, 299–311 (2015).

    Article  CAS  Google Scholar 

  2. GTEx Consortium. Science 348, 648–660 (2015).

  3. Murphy, D., Singh, R., Kolandaivelu, S., Ramamurthy, V. & Stoilov, P. Mol. Cell. Biol. 35, 1860–1870 (2015).

    Article  CAS  Google Scholar 

  4. Doench, J.G. et al. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  Google Scholar 

  5. Shi, J. et al. Nat. Biotechnol. 33, 661–667 (2015).

    Article  CAS  Google Scholar 

  6. Mistry, J. et al. Database 2013, bat023 (2013).

    Article  Google Scholar 

  7. Rauscher, B., Heigwer, F., Breinig, M., Winter, J. & Boutros, M. Nucleic Acids Res. 45 D1, D679–D686 (2017).

    Article  CAS  Google Scholar 

  8. Popp, M.W.-L. & Maquat, L.E. Annu. Rev. Genet. 47, 139–165 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank B. Cummings for help with GTEx data processing and the entire Sanjana laboratory for support and advice. F.Z. is supported by the NIH through NIMH (5DP1-MH100706 and 1R01-MH110049), the NSF, the New York Stem Cell Foundation, the Allen Distinguished Investigator Program through The Paul G. Allen Frontiers Group, the Simons and Vallee Foundations, the Howard Hughes Medical Institute, the Skoltech-MIT Next Generation Program, J. and P. Poitras and the Poitras Center for Affective Disorders, R. Metcalfe, and D. Cheng. N.E.S. is supported by the NIH through NHGRI (R00-HG008171) and the Sidney Kimmel Foundation.

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Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Joshua A Meier & Feng Zhang

  2. Department of Brain and Cognitive Sciences, Department of Biological Engineering, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Joshua A Meier & Feng Zhang

  3. New York Genome Center, New York, New York, USA

    Joshua A Meier & Neville E Sanjana

  4. Department of Biology, New York University, New York, New York, USA

    Joshua A Meier & Neville E Sanjana

Authors
  1. Joshua A Meier
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  2. Feng Zhang
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  3. Neville E Sanjana
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Corresponding author

Correspondence to Neville E Sanjana.

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Competing interests

A patent has been filed relating to the described work. F.Z. is a founder and scientific advisor for Editas Medicine and a scientific advisor for Horizon Discovery.

Integrated supplementary information

Supplementary Figure 1 Integration of GTEx data shifts sgRNA targeting toward highly expressed exons.

GUIDES–generated libraries using data for median expression across all GTEx tissues (red) or without using GTEx data (blue) to choose exons to target. For each library, 500 genes were selected at random (n = 1000 randomized libraries) from the human genome and GUIDES was instructed to design 5 sgRNAs per gene in the selected exons. On average, incorporation of GTEx data increases average expression of targeted exons by a factor of 1.5.

Supplementary Figure 2 Aggregate cut-frequency determination (CFD) off-target score reduces predicted off-target sites for selected sgRNAs.

GUIDES calculates the sum of the cut-frequency determination (CFD) score4 for all 0–3 bp mismatches in the human/mouse exome (“Gscore”). When optimizing sgRNAs for off-target avoidance/specificity, designed human and mouse libraries (n = 2,000 genes) have fewer sgRNAs with a high G-score (i.e. fewer sgRNAs with 0–3 bp potential exome off-targets).

Supplementary Figure 3 On-target scores at each stage of the GUIDES pipeline.

Average on-target scores for a sgRNA library targeting 2,654 transcription factors in the human genome with 3 sgRNAs per gene (a) or 6 sgRNAs per gene (b). On-target (efficiency) scores were calculated using the Microsoft Azimuth algorithm as in ref. 4. Despite on-target optimization being the last stage of the GUIDES pipeline (see Supplementary methods ), on-target score optimization increases the average on-target score of chosen sgRNAs even after other optimizations such as off-target- and protein domain-based prioritization.

Supplementary Figure 4 GUIDES-selected sgRNAs targeted essential genes display greater depletion in a meta-analysis of 77 pooled CRISPR screens.

(a) Cumulative density function of GenomeCRISPR sgRNA effect scores for GUIDES-selected sgRNAs versus a matched-size sample of sgRNAs targeting the same genes randomly chosen from the GenomeCRISPR database. (b) Probability density function for the pergene data shown in (a). The average increase in depletion by using GUIDES-generated sgRNAs over the size-matched randomly selected sets was 0.73 sgRNA effect (∼10% increased depletion, n = 403 genes examined in 77 genome-scale screens using 61 different cell lines), which is significantly greater depletion (p = 5e-07, t = −5.1, df = 409, two-sample paired t-test).

Supplementary Figure 5 Library generation time scales linearly with number of genes targeted.

The indicated number of genes was selected from the human genome and the time required for library generation was tracked as a function of the number of sgRNAs per gene. Gene count and generation time are linearly correlated (r2>0.99 over a range of 5 – 20,000 genes targeted for 3, 6, 10, and 20 sgRNAs/gene, n = 100 library generation runs, error bars indicate s.d.). Since all potential sgRNAs for each gene are precomputed, generation times are not affected by the number of sgRNAs. For benchmarking, GUIDES was run on a computer with a 2.5 GHz Intel Core i7 processor and 16 GB of memory running Linux (Ubuntu 14).

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Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Methods (PDF 2433 kb)

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Meier, J., Zhang, F. & Sanjana, N. GUIDES: sgRNA design for loss-of-function screens. Nat Methods 14, 831–832 (2017). https://doi.org/10.1038/nmeth.4423

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