In the last two decades the study of changes in the genome function that are not induced by changes in DNA has consolidated a strong research field called "epigenetics". Together with special proteins---so-called histones---the DNA forms the nucleosomes, which is the material that builds the chromosome in eukaryotic cells. These histones can be modified by several molecules, thereby changing, for example, the readability of the DNA wrapped around these proteins. Therefore, these changes of the chromatin states are interesting for studying the differentiation of cell types, since the DNA does not change during the differentiation. For this reason, studying the changes of the histone modifications is interesting for Bio-Informaticians.


Visualization can support studying the changes of the histone modifications in many respects.

• In-situ visualizations support understanding the data and the data transformation steps.
  The peak-caller “Sierra Platinum” supports generating the peaks for multiple replicate chromatin data.
  To assess the properties of the individual data sets—experiments as well as backgrounds—statistical visualizations of their properties and computed in-situ and presented to the analyst.
  This allows the analyst to assess the quality of the individual data sets as well as their correlations and their contributions to the peaks generated.
  The Service is accessible via the link:
  Sierra Platinum as a Service
• “Masakari” combines segmenting ChIP-seq data with visualizations supporting the statistical analysos of the segmentation. It is conceived to allow a first analysis of the segmented data assessing the quality of the data and its segmentation. Moreover, it allows to adapt the parameters to obtain more suitable segmentations depending on the data and its quality.
• Available visualizations methods were not suitable for analyzing the relationship between the combinatorial patterns of histone modifications and their regulatory effects. Therefore, we develop new strategies for visualizing histone modification patterns and their changes in different cell types. These visualizations support the analysis of global trends between different cell types or points in time of the whole genome. Additionally, it is possible to analyze regions of interest in the genome.

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