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  Prediction of cardiac transcription networks based on molecular data and complex clinical phenotypes

Toenjes, M., Schueler, M., Hammer, S., Pape, U. J., Fischer, J. J., Felix Berger, F., et al. (2008). Prediction of cardiac transcription networks based on molecular data and complex clinical phenotypes. Molecular BioSystems: A New High Quality Chemical Biology Journal with A Particular Focus on the Interface between Chemistry and the -Omic Sciences and Systems Biology, 4(6), 589-598. doi:10.1039/b800207j.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8011-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8012-3
Genre: Journal Article

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 Creators:
Toenjes, Martje1, Author              
Schueler, Markus1, Author              
Hammer, Stefanie2, Author
Pape, Utz J.3, Author              
Fischer, Jenny J.1, Author              
Felix Berger, Felix2, Author
Vingron, Martin4, Author              
Sperling, Silke1, Author              
Affiliations:
1Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              
2Max Planck Society, ou_persistent13              
3Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
4Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Abstract: We present an integrative approach combining sophisticated techniques to construct cardiac gene regulatory networks based on correlated gene expression and optimized prediction of transcription factor binding sites. We analyze transcription levels of a comprehensive set of 42 genes in biopsies derived from hearts of a cohort of 190 patients as well as healthy individuals. To precisely describe the variety of heart malformations observed in the patients, we delineate a detailed phenotype ontology that allows description of observed clinical characteristics as well as the definition of informative meta-phenotypes. Based on the expression data obtained by real-time PCR we identify specific disease associated transcription profiles by applying linear models. Furthermore, genes that show highly correlated expression patterns are depicted. By predicting binding sites on promoter settings optimized using a cardiac specific chromatin immunoprecipitation data set, we reveal regulatory dependencies. Several of the found interactions have been previously described in literature, demonstrating that the approach is a versatile tool to predict regulatory networks.

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Language(s): eng - English
 Dates: 2008-04-02
 Publication Status: Published in print
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Title: Molecular BioSystems : A New High Quality Chemical Biology Journal with A Particular Focus on the Interface between Chemistry and the -Omic Sciences and Systems Biology
Source Genre: Journal
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Pages: - Volume / Issue: 4 (6) Sequence Number: - Start / End Page: 589 - 598 Identifier: ISSN: 1742-206X