Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

0
3


  • 1.

    Giaever, G. et al. Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat. Genet. 21, 278–283 (1999).

  • 2.

    Parsons, A. B. et al. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat. Biotechnol. 22, 62–69 (2004).

  • 3.

    Parsons, A. B. et al. Exploring the mode-of-action of bioactive compounds by chemical-genetic profiling in yeast. Cell 126, 611–625 (2006).

  • 4.

    Pierce, S. E., Davis, R. W., Nislow, C. & Giaever, G. Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures. Nat. Protoc. 2, 2958–2974 (2007).

  • 5.

    Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

  • 6.

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

  • 7.

    Hoepfner, D. et al. High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions. Microbiol. Res. 169, 107–120 (2014).

  • 8.

    Lee, A. Y. et al. Mapping the cellular response to small molecules using chemogenomic fitness signatures. Science 344, 208–211 (2014).

  • 9.

    Estoppey, D. et al. Identification of a novel NAMPT inhibitor by CRISPR/Cas9 chemogenomic profiling in mammalian cells. Sci. Rep. 7, 42728 (2017).

  • 10.

    Piotrowski, J. S. et al. Functional annotation of chemical libraries across diverse biological processes. Nat. Chem. Biol. 13, 982–993 (2017).

  • 11.

    Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405–410 (2008).

  • 12.

    Ryan, C. J. et al. Hierarchical modularity and the evolution of genetic interactomes across species. Mol. Cell 46, 691–704 (2012).

  • 13.

    Frost, A. et al. Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions. Cell 149, 1339–1352 (2012).

  • 14.

    Vizeacoumar, F. J. et al. A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities. Mol. Syst. Biol. 9, 696 (2013).

  • 15.

    Babu, M. et al. Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli. PLoS Genet. 10, e1004120 (2014).

  • 16.

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

  • 17.

    Hillenmeyer, M. E. et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365 (2008).

  • 18.

    Wildenhain, J. et al. Prediction of synergism from chemical-genetic interactions by machine learning. Cell Syst. 1, 383–395 (2015).

  • 19.

    Smith, A. M. et al. Quantitative phenotyping via deep barcode sequencing. Genome Res. 19, 1836–1842 (2009).

  • 20.

    Smith, A. M. et al. Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples. Nucleic Acids Res. 38, e142 (2010).

  • 21.

    Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

  • 22.

    Cleveland, W. S. LOWESS: a program for smoothing scatterplots by robust locally weighted regression. Am. Stat. 35, 54 (1981).

  • 23.

    Yang, Y. H. with contributions from Paquet, A. & Dudoit, S. marray: Exploratory analysis for two-color spotted microarray data. https://rdrr.io/bioc/marray/ (2009).

  • 24.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  • 25.

    Piotrowski, J. S. et al. Chemical genomic profiling via barcode sequencing to predict compound mode of action. Methods Mol. Biol. 1263, 299–318 (2015).

  • 26.

    Piotrowski, J. S. et al. Plant-derived antifungal agent poacic acid targets β-1,3-glucan. Proc. Natl. Acad. Sci. USA 112, E1490–E1497 (2015).

  • 27.

    Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods 7, 1017–1024 (2010).

  • 28.

    Morales, E. H. et al. Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite. Nat. Commun. 8, 15320 (2017).

  • 29.

    Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

  • 30.

    Ho, C. H. et al. A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds. Nat. Biotechnol. 27, 369–377 (2009).

  • 31.

    Ben-Aroya, S. et al. Toward a comprehensive temperature-sensitive mutant repository of the essential genes of Saccharomyces cerevisiae. Mol. Cell 30, 248–258 (2008).

  • 32.

    Spirek, M. et al. S. pombe genome deletion project: an update. Cell Cycle 9, 2399–2402 (2010).

  • 33.

    Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

  • 34.

    Andrusiak, K. Adapting S. cerevisiae Chemical Genomics for Identifying the Modes of Action of Natural Compounds. Master’s thesis, University of Toronto (2012).

  • 35.

    Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

  • 36.

    Bao, E., Jiang, T., Kaloshian, I. & Girke, T. SEED: efficient clustering of next-generation sequences. Bioinformatics 27, 2502–2509 (2011).

  • 37.

    Shimizu, K. & Tsuda, K. SlideSort: all pairs similarity search for short reads. Bioinformatics 27, 464–470 (2011).

  • 38.

    Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2, e593 (2014).

  • 39.

    Zorita, E., Cuscó, P. & Filion, G. J. Starcode: sequence clustering based on all-pairs search. Bioinformatics 31, 1913–1919 (2015).

  • 40.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

  • 41.

    Vetrovský, T., Baldrian, P., Morais, D. & Berger, B. SEED 2: a user-friendly platform for amplicon high-throughput sequencing data analyses. Bioinformatics 34, (2018).

  • 42.

    Zhao, L., Liu, Z., Levy, S. F. & Wu, S. Bartender: a fast and accurate clustering algorithm to count barcode reads. Bioinformatics 34, 739–747 (2018).

  • 43.

    Dai, Z. et al. edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens. F1000Res. 3, 95 (2014).

  • 44.

    Mun, J., Kim, D.-U., Hoe, K.-L. & Kim, S.-Y. Genome-wide functional analysis using the barcode sequence alignment and statistical analysis (Barcas) tool. BMC Bioinformatics 17, 475 (2016).

  • 45.

    Robinson, D. G., Chen, W., Storey, J. D. & Gresham, D. Design and analysis of Bar-seq experiments. G3 (Bethesda) 4, 11–18 (2014).

  • 46.

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

  • 47.

    Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).

  • 48.

    Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

  • 49.

    Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

  • 50.

    Simpkins, S. W. et al. Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks. Preprint at https://www.biorxiv.org/content/early/2018/05/18/111252 (2018).



  • Source link

    LEAVE A REPLY

    Please enter your comment!
    Please enter your name here