Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.
Bibliographical noteFunding Information:
University of Minnesota Doctoral Dissertation Fellowship (to B.V.); National Institutes of Health grants [1R01HG005853-01, 1R01HG005084-01A1, 1R01HG005853-01 to B.V., M.C., C.L.M., B.A., C.B.]; CIHR grant [MOP-102629 to B.A., C.B.]; ORF grant [GL2-01-22 to B.A., C.B.]; NSERC Doctoral Postgraduate Scholarship (to E.K.); and Canadian Institute for Advanced Research Fellowship (to L.P.). Funding for open access charge: Personal funds, or group/institutional grants.