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  Network and Pathway Analysis of Toxicogenomics Data

Barel, G., & Herwig, R. (2018). Network and Pathway Analysis of Toxicogenomics Data. Frontiers in Genetics, 9: 9:484. doi:10.3389/fgene.2018.00484.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-A011-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-A012-4
Genre: Journal Article

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© 2018 Barel and Herwig

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 Creators:
Barel, Gal1, Author              
Herwig, Ralf1, Author              
Affiliations:
1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              

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Free keywords: network analysis, protein–protein interaction network, pathways, drug toxicity, toxicogenomics, transcriptomics, anthracyclines
 Abstract: Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

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Language(s): eng - English
 Dates: 2018-10-22
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/fgene.2018.00484
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Title: Frontiers in Genetics
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Media
Pages: - Volume / Issue: 9 Sequence Number: 9:484 Start / End Page: - Identifier: ISSN: 1664-8021
CoNE: https://pure.mpg.de/cone/journals/resource/1664-8021