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Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells
Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is m...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171340/ https://www.ncbi.nlm.nih.gov/pubmed/18354733 http://dx.doi.org/10.1155/2007/46150 |
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author | Niemistö, Antti Nykter, Matti Aho, Tommi Jalovaara, Henna Marjanen, Kalle Ahdesmäki, Miika Ruusuvuori, Pekka Tiainen, Mikko Linne, Marja-Leena Yli-Harja, Olli |
author_facet | Niemistö, Antti Nykter, Matti Aho, Tommi Jalovaara, Henna Marjanen, Kalle Ahdesmäki, Miika Ruusuvuori, Pekka Tiainen, Mikko Linne, Marja-Leena Yli-Harja, Olli |
author_sort | Niemistö, Antti |
collection | PubMed |
description | Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/. |
format | Online Article Text |
id | pubmed-3171340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Springer |
record_format | MEDLINE/PubMed |
spelling | pubmed-31713402011-09-13 Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells Niemistö, Antti Nykter, Matti Aho, Tommi Jalovaara, Henna Marjanen, Kalle Ahdesmäki, Miika Ruusuvuori, Pekka Tiainen, Mikko Linne, Marja-Leena Yli-Harja, Olli EURASIP J Bioinform Syst Biol Research Article Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/. Springer 2007-08-19 /pmc/articles/PMC3171340/ /pubmed/18354733 http://dx.doi.org/10.1155/2007/46150 Text en Copyright © 2007 Antti Niemistö et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Niemistö, Antti Nykter, Matti Aho, Tommi Jalovaara, Henna Marjanen, Kalle Ahdesmäki, Miika Ruusuvuori, Pekka Tiainen, Mikko Linne, Marja-Leena Yli-Harja, Olli Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title | Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title_full | Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title_fullStr | Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title_full_unstemmed | Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title_short | Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells |
title_sort | computational methods for estimation of cell cycle phase distributions of yeast cells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171340/ https://www.ncbi.nlm.nih.gov/pubmed/18354733 http://dx.doi.org/10.1155/2007/46150 |
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