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Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute

Nature Protocols volume 14, pages 756–780 (2019)Cite this article

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Abstract

Genome-wide screening using CRISPR coupled with nuclease Cas9 (CRISPR–Cas9) is a powerful technology for the systematic evaluation of gene function. Statistically principled analysis is needed for the accurate identification of gene hits and associated pathways. Here, we describe how to perform computational analysis of CRISPR screens using the MAGeCKFlute pipeline. MAGeCKFlute combines the MAGeCK and MAGeCK-VISPR algorithms and incorporates additional downstream analysis functionalities. MAGeCKFlute is distinguished from other currently available tools by its comprehensive pipeline, which contains a series of functions for analyzing CRISPR screen data. This protocol explains how to use MAGeCKFlute to perform quality control (QC), normalization, batch effect removal, copy-number bias correction, gene hit identification and downstream functional enrichment analysis for CRISPR screens. We also describe gene identification and data analysis in CRISPR screens involving drug treatment. Completing the entire MAGeCKFlute pipeline requires ~3 h on a desktop computer running Linux or Mac OS with R support.

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Fig. 1: Schematic representation of CRISPR–Cas9 screen analysis using MAGeCKFlute.
Fig. 2: Example quality control assessment of CRISPR–Cas9 screen data.
Fig. 3: Batch effect correction and normalization of read counts and beta scores from CRISPR screen data.
Fig. 4: CRISPR–Cas9 screen analysis by MAGeCKFlute.

Data availability

The source code of MAGeCKFlute (version 0.99.18) is freely available at https://bitbucket.org/liulab/mageckflute/ under the three-clause Berkeley Software Distribution (BSD) open-source license. Questions or comments can be submitted through the MAGeCK Google group: https://groups.google.com/d/forum/mageck. The datasets used in this paper are presented in http://cistrome.org/MAGeCKFlute/.

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Acknowledgements

This project was supported by the National Institutes of Health (R01 HG008927), the National Key Research and Development Program of China (2017YFC0908500 to X.S.L), the Breast Cancer Research Foundation, the Department of Defense (PC140817P1 to M.B. and X.S.L), and the start-up fund of the Center for Genetic Medicine Research and the Gilbert Family Neurofibromatosis Institute (to W.L.).

Author information

Author notes

  1. Tengfei Xiao

    Present address: GV20 Oncotherapy, Shanghai, China

  2. Wei Li

    Present address: Center for Genetic Medicine Research, Children’s National Medical Center, Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA

  3. These authors contributed equally: Binbin Wang, Mei Wang, Wubing Zhang.

Authors and Affiliations

  1. Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Binbin Wang, Wubing Zhang, Ziyi Li, Shenglin Mei, Sailing Shi & X. Shirley Liu

  2. Department of Geriatrics, Shanghai General Hospital, Shanghai, China

    Mei Wang

  3. Center for Functional Cancer Epigenetics, Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Tengfei Xiao, Chen-Hao Chen, Alexander Wu, Nicole Traugh, Xiaoqing Wang, Myles Brown, Wei Li & X. Shirley Liu

  4. Program in Computational Biology and Quantitative Genetics, Harvard School of Public Health, Boston, MA, USA

    Alexander Wu

  5. Laboratory of Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Feizhen Wu

  6. Key Laboratory of Birth Defects, Children’s Hospital of Fudan University, Shanghai, China

    Feizhen Wu

  7. School of Computer, National University of Defense Technology, Changsha, China

    Yingbo Cui

  8. Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria

    Jesse Jonathan Lipp, Matthias Hinterndorfer & Johannes Zuber

  9. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Myles Brown

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Contributions

W.L. and X.S.L. developed the original MAGeCK and MAGeCK-VISPR algorithm. B.W., M.W. and W.Z. developed the R package MAGeCKFlute. B.W. and W.Z. performed the data analysis; B.W., M.W., F.W., W.L. and X.S.L. wrote the manuscript with the help of Z.L., N.T. and X.W. W.L., X.S.L., B.W., M.W., W.Z., F.W., Z.L., N.T., X.W., T.X., C.-H.C., A.W., S.M., Y.C., S.S., J.J.L., M.H., J.Z. and M.B. contributed to the discussion and writing of the final manuscript.

Corresponding authors

Correspondence to Wei Li or X. Shirley Liu.

Ethics declarations

Competing interests

T.X. and X.S.L are co-founders and M.B. and X.S.L. are on the Scientific Advisory Board of GV20 Oncotherapy. The authors declare no competing interests.

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Related links

Key references using this protocol

Li, W. et al. Genome Biol. 15, 554 (2014): https://doi.org/10.1186/s13059-014-0554-4

Li, W. et al. Genome Biol. 16, 281 (2015): https://doi.org/10.1186/s13059-015-0843-6

Jeselsohn, R. et al. Cancer Cell 33, 173–186 (2018): https://doi.org/10.1016/j.ccell.2018.01.004

Xiao, T. et al. Proc. Natl Acad. Sci. USA 115, 7869–7878 (2018): https://doi.org/10.1073/pnas.1722617115

Key data used in this protocol

Toledo, C. M. et al. Cell Rep. 13, 2425–2439 (2015): https://doi.org/10.1016/j.celrep.2015.11.021

Hart, T. et al. Cell 163, 1515–1526 (2015): https://doi.org/10.1016/j.cell.2015.11.015

Shalem, O. et al. Science 343, 84–87 (2014): https://doi.org/10.1126/science.1247005

Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Science 343, 80–84 (2014): https://doi.org/10.1126/science.1246981

Chen, C.-H. et al. Bioinformatics 34, 4095–4101 (2018): https://doi.org/10.1093/bioinformatics/bty450

Integrated supplementary information

Supplementary Figure 1 Selection of non-essential genes for normalization of CRIPSR screen data.

(a) Distribution of expression of all non-essential genes in CCLE cell lines. The x-axis is the relative expression of all non-essential genes measured by microarray. The y-axis is the density of expression of all non-essential genes. Genes with expression levels below the cutoff (red dashed line) were excluded from the non-essential gene list. (b) The coordinate of each dot indicates the number of genes (y-axis) whose expression ranked between the 5th and 100th percentile of the number of cell lines (x-axis). The dashed lines indicate that there are 350 out of 937 non-essential genes had expression that ranked between the 5th and 100th percentile in 98.3% (1019 out of 1036) Cancer Cell Line Encyclopedia (CCLE)36 cell lines.

Supplementary Figure 2 Copy number bias correction in MAGeCK-MLE.

Model of the relationship between β scores and gene copy numbers before (a) and after (b) copy number correction. The red line of each panel is the regression line, and the inflection point is calculated by minimizing the least squared error. Without the copy number bias correlation, the beta score shows a positive correlation with copy number. This bias can be corrected using MAGeCKFlute.

Supplementary Figure 3 Normalization with essential genes.

Beta score of core essential genes (blue dots) and all genes except essential genes (red dots) before and after normalization with essential genes. The histograms (blue bars) show the beta scores of treatment (top) and control (right) conditions. Before normalization (a), the beta score distribution of treatment and control conditions are not comparable. After normalization (b), these two distributions are more comparable (c) The formula for normalization of the beta score using essential genes where c is an empirical value is used to scale the normalized beta score. The value of c is 0.6 and was obtained from public screen data8.

Supplementary Figure 4 Output figures of MAGeCKFlute.

All the data are from a genome-wide CRRSPR screen on the A375 cell line (EQUIPMENT) and downstream analysis was performed with FluteMLE (a) Beta score distribution of treatment samples (PLX7_R1, PLX7_R2) and control samples (D7_R1, D7_R2). (b) Scatterplot of beta scores of treatment (PLX7_R1) and control (D7_R1) sample. The regression line (dashed line) indicates the consistency of beta scores between the two conditions. (c) The MA plot can be used to visualize the differences between beta scores in two samples, by transforming the data onto M (log ratio) and A (mean average) scales, in which M= βT-βC, A=βT+βC, βT is the beta score of treatment samples, βC is the beta score of control samples. Blue line is M=0 and red line is the loess regression line. (d) Identification of treatment related genes. The horizontal and vertical dashed lines indicate the mean plus or minus one stand deviation of treatment and control beta score, respectively. The diagonal dashed line indicates mean plus or minus one standard deviation of the differential beta score which can be calculated by subtracting the control from the treatment beta score. The number in red is the number of genes classified in each group. Top 5 genes are selected based on the largest absolute value of the differential beta score and labelled in each group. Genes in the green group are strongly negatively selected in the control samples and are weakly positively or negatively selected in the treatment samples. These genes are potentially located in the pathways targeted by the treatment. The orange group contains genes that are weakly selected in the control and strongly positively selected in treatment. These are genes whose loss confers treatment resistance. Genes in the blue group are strongly positively selected in the control and weakly selected in the treatment. These genes may be either potential regulators of cell proliferation in general, or regulators of the treatment target. Genes in the purple group are weakly selected in the control and strongly negatively selected in the treatment. These genes are potentially synthetically lethal in combination with the drug treatment. The histograms (grey bars) show the beta scores of treatment (top) and control (right) conditions.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Methods

Reporting Summary

Supplementary Data 1

The nonessential gene list.

Supplementary Data 2

Copy-number file used to perform the copy-number correction.

Supplementary Data 3

The list of core essential genes.

Supplementary Data 4

The LNCap data, which include AAVS1, CCR5 and ROSA26 as negative-control genes.

Supplementary Video 1

A video tutorial showing how to edit the ‘config.yaml’ file used by MAGeCK-VISPR.

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Wang, B., Wang, M., Zhang, W. et al. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat Protoc 14, 756–780 (2019). https://doi.org/10.1038/s41596-018-0113-7

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