Biomedical subjects

Fumi Sano

Publications and source records attributed to Fumi Sano.

4 recordsLinked to original sources

PubMed2005

High-dimensional and large-scale phenotyping of yeast mutants.

One of the most powerful techniques for attributing functions to genes in uni- and multicellular organisms is comprehensive analysis of mutant traits. In this study, systematic and quantitative analyses of mutant traits are achieved in the budding yeast Saccharomyces cerevisiae by investigating morphological phenotypes. Analysis of fluorescent microscopic images of triple-stained cells makes it possible to treat morphological variations as quantitative traits. Deletion of nearly half of the yeast genes not essential for growth affects these morphological traits. Similar morphological phenotypes are caused by deletions of functionally related genes, enabling a functional assignment of a locus to a specific cellular pathway. The high-dimensional phenotypic analysis of defined yeast mutant strains provides another step toward attributing gene function to all of the genes in the yeast genome.

Actins↗

PubMed2005

Data mining tools for the Saccharomyces cerevisiae morphological database.

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/.

Computer Graphics↗

PubMed2004

SCMD: Saccharomyces cerevisiae Morphological Database.

To study the global regulation of cell morphology, a number of groups have recently reported genome-wide screening data for yeast mutants with abnormal morphology. Despite the relatively simple ellipsoidal shape of yeast cells, in the past, cell morphology researchers have processed information on cells manually. These time-consuming, entirely subjective tasks motivated us to develop image-processing software that automatically extracts yeast cells from micrographs and processes them to measure key morphological characteristics such as cell size, roundness, bud neck position angle, nuclear DNA localization and actin localization. To date, we have retrieved 960,609 cells from 52,988 micrographs of 2531 mutants using our software, and we have published the results in the Saccharomyces cerevisiae Morphological Database (SCMD), which facilitates the analysis of abnormal cells. Our system provides quantitative data for shapes of the daughter and mother cells, localization of the nuclear DNA and morphology of the actin patches. To search for mutants with similar morphological traits, the system outputs a list of mutants ranked by similarity of average morphological parameters. The SCMD is available at http://yeast. gi.k.u-tokyo.ac.jp/.

Cell Cycle↗

PubMed2004

Development of image processing program for yeast cell morphology.

Every living organism has its own species-specific morphology. Despite the relatively simple ellipsoidal shape of budding yeast cells, the global regulation of yeast morphology remains unclear. In the past, each mutated gene from many mutants with abnormal morphology had to be classified manually. To investigate the morphological characteristics of yeast in detail, we developed a novel image-processing program that extracts quantitative data from microscope images automatically. This program extracts data on cells that are often used by yeast morphology researchers, such as cell size, roundness, bud neck position angle, and bud growth direction, and fits an ellipse to the cell outline. We evaluated the ability of the program to extract quantitative parameters. The results suggest that our image-processing program can play a central objective role in yeast morphology studies.

Computational Biology↗