CYCLoPs (Collection of Yeast Cell and Localization Patterns) consists of high-throughput imaging screens of yeast GFP-tagged strains, as well as the analysis of protein abundance and localization, which was obtained using support vector machines and convolutional neural networks, as described in Chong et al. and Kraus et al. (Chong et al. 2015; Kraus et al. 2017). The GFP collection was imaged in standard conditions using confocal microscopy, in the presence of 3 chemicals (HU – hydroxyurea; RAP – rapamycin, AF – alpha factor), and one mutant background (deletion of RPD3 gene). Each field of view was imaged in two channels: the green (GFP) channel represents a GFP-fused protein from the ORF-GFP collection (Huh et al. 2003), while the red (RFP) channel identifies the cytosol and was used for cell segmentation purposes. In total, the CYCLoPs dataset covers 4,139 proteins from S. cerevisiae. These proteins have been classified into 16 localization classes.
To retrieve micrographs, protein localization and abundance data:
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