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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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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).
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Methods (PDF 2433 kb)
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Life Sciences Reporting Summary (PDF 67 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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DOI: https://doi.org/10.1038/nmeth.4423
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