MOSAIC: A chemical-genetic interaction data repository and web resource for exploring chemical modes of action

Justin Nelson, Scott W. Simpkins, Hamid Safizadeh, Sheena C. Li, Jeff S. Piotrowski, Hiroyuki Hirano, Yoko Yashiroda, Hiroyuki Osada, Minoru Yoshida, Charles Boone, Chad L. Myers

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-Throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-Action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-Action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation MOSAIC is available at Contact,, or Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1251-1252
Number of pages2
Issue number7
StatePublished - Apr 1 2018

Bibliographical note

Funding Information:
This work was partially supported by the National Institutes of Health [R01HG005084, R01GM104975]; the National Science Foundation [DBI 0953881 to C.M.]; the Canadian Institutes of Health Research [FDN-143264 to C.B.]; and the JSPS KAKENHI [15H04483 to C.B.]. M.Y. is supported by Japan Society for the Promotion of Science KAKENHI [26221204]. H.O. is a research fellow of the Japan Society for the Promotion of Science. S.W.S. was supported by an NSF Graduate Research Fellowship [00039202], an NIH Biotechnology Training Grant [T32GM008347] and a one year BICB fellowship from the University of Minnesota. S.C.L. is funded by a RIKEN Incentive Research Projects Grant [FY2017].

Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.


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