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Wildenhain J, Spitzer M, Dolma S, et al. Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism. Sci Data. 2016;3:160095doi: 10.1038/sdata.2016.95.
Wildenhain, J., Spitzer, M., Dolma, S., Jarvik, N., White, R., Roy, M., Griffiths, E., Bellows, D. S., Wright, G. D., & Tyers, M. (2016). Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism. Scientific data, 3160095. https://doi.org/10.1038/sdata.2016.95
Wildenhain, Jan, et al. "Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism." Scientific data vol. 3 (2016): 160095. doi: https://doi.org/10.1038/sdata.2016.95
Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism. Sci Data. 2016 Nov 22;3:160095. doi: 10.1038/sdata.2016.95. PMID: 27874849; PMCID: PMC5127411.
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Sci Data. 2016 Nov 22;3:160095. doi: 10.1038/sdata.2016.95.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism.

Scientific data

Jan Wildenhain, Michaela Spitzer, Sonam Dolma, Nick Jarvik, Rachel White, Marcia Roy, Emma Griffiths, David S Bellows, Gerard D Wright, Mike Tyers

Affiliations

  1. Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3JR, UK.
  2. Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada L8N 3Z5.
  3. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada M5G 1X5.
  4. Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec, Canada H3C 3J7.

PMID: 27874849 PMCID: PMC5127411 DOI: 10.1038/sdata.2016.95

Abstract

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.

Conflict of interest statement

The authors declare no competing financial interests.

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