ArticleBioinformatics (Oxford, England)2018
MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis.Nature communications · 2025Article
- Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.Journal of natural products · 2025Article
- Antifungal discovery.Current opinion in microbiology · 2022Review
- Genomic Approaches to Antifungal Drug Target Identification and Validation.Annual review of microbiology · 2022Review
- Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.Nature communications · 2021Article
- Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.Journal of chemical information and modeling · 2021Article
- Advances in fungal chemical genomics for the discovery of new antifungal agents.Annals of the New York Academy of Sciences · 2021Review
- A marine microbiome antifungal targets urgent-threat drug-resistant fungi.Science (New York, N.Y.) · 2020Article
- Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.Nature biotechnology · 2020Article
- Article
- Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.PLoS computational biology · 2018Article
- Quantitative Yeast Genetic Interaction Profiling of Bacterial Effector Proteins Uncovers a Role for the Human Retromer in Salmonella Infection.Cell systems · 2018Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Summary: 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 http://mosaic.cs.umn.edu. Contact: hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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