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An integrative clustering and modeling algorithm for dynamical gene expression data
Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge. Re...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117368/ https://www.ncbi.nlm.nih.gov/pubmed/21685097 http://dx.doi.org/10.1093/bioinformatics/btr250 |
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author | Sivriver, Julia Habib, Naomi Friedman, Nir |
author_facet | Sivriver, Julia Habib, Naomi Friedman, Nir |
author_sort | Sivriver, Julia |
collection | PubMed |
description | Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge. Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response. Availability: The code to our method is freely available http://www.compbio.cs.huji.ac.il/DynaMiteC. Contact: nir@cs.huji.ac.il |
format | Online Article Text |
id | pubmed-3117368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-31173682011-06-17 An integrative clustering and modeling algorithm for dynamical gene expression data Sivriver, Julia Habib, Naomi Friedman, Nir Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge. Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response. Availability: The code to our method is freely available http://www.compbio.cs.huji.ac.il/DynaMiteC. Contact: nir@cs.huji.ac.il Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117368/ /pubmed/21685097 http://dx.doi.org/10.1093/bioinformatics/btr250 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Sivriver, Julia Habib, Naomi Friedman, Nir An integrative clustering and modeling algorithm for dynamical gene expression data |
title | An integrative clustering and modeling algorithm for dynamical gene expression data |
title_full | An integrative clustering and modeling algorithm for dynamical gene expression data |
title_fullStr | An integrative clustering and modeling algorithm for dynamical gene expression data |
title_full_unstemmed | An integrative clustering and modeling algorithm for dynamical gene expression data |
title_short | An integrative clustering and modeling algorithm for dynamical gene expression data |
title_sort | integrative clustering and modeling algorithm for dynamical gene expression data |
topic | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117368/ https://www.ncbi.nlm.nih.gov/pubmed/21685097 http://dx.doi.org/10.1093/bioinformatics/btr250 |
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