Data mining tools for the Saccharomyces cerevisiae morphological database

Abstract

For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1,899,247 cell images was assembled from 91,71 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure approximately 500 morphological parameters in individual mutants. We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web at http://scmd.gi.k.u-tokyo.ac.jp/.