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Data mining tools for the Saccharomyces cerevisiae morphological database.

Taro L Saito1, Jun Sese, Yoichiro Nakatani

  • 1Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

Nucleic Acids Research
|June 28, 2005
PubMed
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This study analyzed 1.9 million yeast cell images to quantify morphological changes from gene mutations. The Saccharomyces cerevisiae Morphological Database (SCMD) and new tools aid in discovering gene functions through cell shape analysis.

Area of Science:

  • Cell biology
  • Genetics
  • Bioinformatics

Background:

  • Understanding gene function requires detailed analysis of cellular morphology.
  • Loss-of-function mutations in budding yeast can cause significant, yet subtle, morphological changes.
  • A comprehensive dataset of yeast cell morphology is needed for systematic study.

Purpose of the Study:

  • To create a large-scale, quantitative morphological dataset for budding yeast mutants.
  • To develop computational tools for analyzing and visualizing morphological data.
  • To facilitate the discovery of gene functions by linking morphology to genetic disruptions.

Main Methods:

  • Assembled a dataset of 1,899,247 cell images from 4782 budding yeast mutants.
  • Computationally analyzed images to extract ~500 morphological parameters per mutant.

Related Experiment Videos

  • Developed software for visualizing parameter distributions and identifying discriminating features.

    • Main Results:

    • Established the Saccharomyces cerevisiae Morphological Database (SCMD) with quantitative morphological data.
    • Created visualization tools to easily identify significant morphological changes in mutants.
    • Developed a system to automatically identify combinations of morphological parameters that characterize gene functions.

    Conclusions:

    • The SCMD provides a valuable resource for studying yeast cell morphology and gene function.
    • Computational analysis and data visualization are powerful tools for biological discovery.
    • Linking quantitative morphology to genetic perturbations offers new insights into cellular processes.