A genome wide RNAI screen by time lapse microscopy in order to identify mitotic genes - computational aspects and challenges
The MitoCheck project aims at identifying and characterizing the function of genes involved in mitosis in live human cells. The genome wide RNA interference screen developed for this purpose is based on automatic time-lapse microscopy to analyze the phenotypes after knocking down each human gene ind...
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creator | Walter, T. Held, M. Neumann, B. Heriche, J.-K. Conrad, C. Pepperkok, R. Ellenberg, J. |
description | The MitoCheck project aims at identifying and characterizing the function of genes involved in mitosis in live human cells. The genome wide RNA interference screen developed for this purpose is based on automatic time-lapse microscopy to analyze the phenotypes after knocking down each human gene individually in cultured cells whose chromosomes are fluorescently labeled. Such a screen produces large amounts of digital image data (~ 200.000 video sequences, i.e. over 18 millions of images) which can no longer be handled and interpreted manually. We have developed an image processing method consisting of segmentation, feature extraction and automatic classification, which assigns to each nucleus in each image one out of several predefined morphological classes. Using the relative cell counts in each of these classes, measured over time for each experiment, we derive a phenotypic fingerprint for each gene that allows clustering of genes by functional similarity. This paper will give an overview over the computational aspects of this screen. The complete quality controlled data set and phenotypic measurements will be available after publication on http://www.mitocheck.org/. |
doi_str_mv | 10.1109/ISBI.2008.4540999 |
format | Conference Proceeding |
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The genome wide RNA interference screen developed for this purpose is based on automatic time-lapse microscopy to analyze the phenotypes after knocking down each human gene individually in cultured cells whose chromosomes are fluorescently labeled. Such a screen produces large amounts of digital image data (~ 200.000 video sequences, i.e. over 18 millions of images) which can no longer be handled and interpreted manually. We have developed an image processing method consisting of segmentation, feature extraction and automatic classification, which assigns to each nucleus in each image one out of several predefined morphological classes. Using the relative cell counts in each of these classes, measured over time for each experiment, we derive a phenotypic fingerprint for each gene that allows clustering of genes by functional similarity. This paper will give an overview over the computational aspects of this screen. The complete quality controlled data set and phenotypic measurements will be available after publication on http://www.mitocheck.org/.</abstract><pub>IEEE</pub><doi>10.1109/ISBI.2008.4540999</doi><tpages>4</tpages></addata></record> |
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ispartof | 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, p.328-331 |
issn | 1945-7928 1945-8452 |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Bioinformatics Biological cells Biomedical Image Processing Cells (biology) Digital images Fluorescence Genome-wide RNAi Screen Genomics Humans Interference Microscopy Mitosis Pattern Recognition RNA |
title | A genome wide RNAI screen by time lapse microscopy in order to identify mitotic genes - computational aspects and challenges |
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