ArticleNucleic acids research2005
Data mining tools for the Saccharomyces cerevisiae morphological database.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An algorithm to automate yeast segmentation and tracking.PloS one · 2013Article
- Efficient framework for automated classification of subcellular patterns in budding yeast.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2009Article
- Yeast Rrp14p is a nucleolar protein involved in both ribosome biogenesis and cell polarity.RNA (New York, N.Y.) · 2007Article
- Evaluation of image processing programs for accurate measurement of budding and fission yeast morphology.Current genetics · 2006Article
- High-dimensional and large-scale phenotyping of yeast mutants.Proceedings of the National Academy of Sciences of the United States of America · 2005Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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/.
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