The late detection of non-genotoxic carcinogens in the drug development process can delay drug candidates for unmet medical needs from reaching the market despite considerable investments in their development. To enable faster, safer, and less expensive development of medications for patients, the MARCAR project generated a large set of transcriptomic data to investigate the underlying mechanisms of non-genotoxic hepatocarcinogenesis and to identify potential biomarkers for early detection of tumor formation in the rodent liver. The effective mining of these high-dimensional datasets is a non-trivial task that usually requires bioinformatics support to extract relevant mechanistic patterns and confirm toxicological hypotheses. Here, we present MARCARviz, a web-platform that enables biologists to (a) quickly address the most common questions associated with the MARCAR microarray data, to (b) identify relevant patterns in the data, and to (c) generate or confirm mechanistic hypotheses about non-genotoxic effects leading to cancer formation. The major advantage of MARCARviz is that there is no software or advanced technical knowledge required to perform powerful analyses and generate visualizations of the MARCAR data. MARCARviz greatly facilitates the confirmation of published MARCAR results and generation of new insights from the collected data by the greater public without the requirement for tedious pre-processing steps. MARCARviz is publicly available from https://tea.cs.uni-tuebingen.de/.
This is an article which has been accepted for the "GCB 2016 Conference".
The authors declare that they have no competing interests. Heidrun Ellinger-Ziegelbauer is an employee of Bayer Pharma AG.
The following information was supplied regarding the deposition of microarray data:
GSE68387
The following information was supplied regarding data availability:
GitHub, https://github.com/mroemer/marcarviz