Clemens Wrzodek

Publications (2)9.56 Total impact

• Article: Pathway-based visualization of cross-platform microarray datasets.

  Clemens Wrzodek, Johannes Eichner, Andreas Zell
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  ABSTRACT: MOTIVATION: Traditionally, microarrays were almost exclusively employed for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs, proteins, and DNA methylation. Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies. Above all, there is a demand for methods that can visualize genomic features that are not directly linked to protein-coding genes, such as regulatory RNAs (e.g., microRNAs) and epigenetic alterations (e.g., DNA methylation), in a pathway-centered manner. RESULTS: We present a novel pathway-based visualization method that is especially suitable for the visualization of high-throughput datasets from multiple different microarray platforms which were employed for the analysis of diverse genomic features in the same set of biological samples. The proposed methodology includes concepts for linking DNA methylation and microRNA expression datasets to canonical signaling and metabolic pathways. We further point out strategies for displaying data from multiple proteins and protein modifications corresponding to the same gene. Ultimately, we show how data from four distinct platform types (mRNA, miRNA, protein, and DNA methylation arrays) can be integratively visualized in the context of canonical pathways. AVAILABILITY: The described method is implemented as part of the InCroMAP application that is freely available at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP. CONTACT: clemens.wrzodek@uni-tuebingen.de.
  Bioinformatics 10/2012; · 5.47 Impact Factor
• Source

  Available from: Florian Mittag

  Article: Linking the epigenome to the genome: correlation of different features to DNA methylation of CpG islands.

  Clemens Wrzodek, Finja Büchel, Georg Hinselmann, Johannes Eichner, Florian Mittag, Andreas Zell
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  ABSTRACT: DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/.
  PLoS ONE 01/2012; 7(4):e35327. · 4.09 Impact Factor