Cellular rush hour

Tools enabling combined analysis of time-dependent omics data

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How can cellular dynamics be defined at multiple levels and in temporal resolution? In order to map these complex processes, both the handling of large data sets as well as elaborated software solutions are required. An application for analyzing large amounts of omics data in terms of time is shown here.

Along the complex path from gene via transcripts to protein many defects can occur. As a result, individual defects or certain defect combinations can lead to diseases. Extensive information on genetic constitution (genome), gene activity (transcriptome) and resulting proteins (proteome) enable us to gain a better understanding of cellular processes. New high-throughput technologies have made it possible to produce enormous amounts of these genomic, transcriptomic and proteomic data (omics data). Integration and linked analysis of these omics-data, already holds a large amount of information. In addition, if one includes the time factor, these data allow conclusions to be drawn on dynamic processes in cells. Owing to the strong dynamics of cellular processes, temporal resolution offers high information content. However, this analysis represents a bioinformatical challenge. e:Med scientists Astrid Wachter and Tim Beißbarth of the MMML-Demonstrators project have developed a software, based on the statistical platform R, that enables pathway-based integration of time-dependent omics data. Using this application (pwOmics), they have studied the signal paths below the EGF-receptor in more detail. This receptor is important for cell growth and is in many cancer diseases generally overactive. In silico-results of e:Med scientists were able to confirm experimental findings and furthermore predicted new interactions. This systems medicine-oriented approach enables us to use large data sets in a broader context and to detect new interactions. This method allows a time-dependent resolution of cellular processes which enables a more precise understanding of biological processes.

Original publications:

Wachter, A., & Beißbarth, T. (2015). pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge. Bioinformatics (Oxford, England), 31(18), 3072-3074. doi.org/10.1093/bioinformatics/btv323

Wachter, A., & Beißbarth, T. (2016). Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data. Frontiers in Genetics, 6, 351. doi.org/10.3389/fgene.2015.00351

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