@article {879,
	title = {Masakari: visualization supported statistical analysis of genome segmentations},
	journal = {BMC Bioinformatics},
	volume = {21},
	year = {2020},
	month = {10/2020},
	chapter = {437},
	abstract = {Background

In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights.

Results and discussion

We propose a new tool called {\textquoteleft}Masakari{\textquoteright} that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data.

Conclusion

Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types.},
	doi = {10.1186/s12859-020-03761-6},
	url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03761-6},
	author = {Dirk Zeckzer and Alrik Hausdorf and Nicole Hinzmann and Lydia M{\"u}ller and Daniel Wiegreffe}
}
@proceedings {807,
	title = {SyCaT-Vis: Visualization-Based Support of Analyzing System Behavior based on System Call Traces},
	year = {2019},
	address = {Pilsen, Czech Republic},
	abstract = {Detecting anomalies in the behavior of a computer system is crucial for determining its security. One way of detecting these anomalies is based on the assessment of the amount and sequence of system calls issued by processes. While the number of processes on a computer can become very large, the number of system calls issued during the lifespan of such a process and its subprocesses can be humongous. In order to decide whether these anomalies are due to the intended system usage or if they are caused by malicious actions, this humongous amount of data needs being analyzed. Thus, a careful analysis of the system calls{\^a}{\texteuro}{\texttrademark} types, their amount, and their temporal sequence requires sophisticated support. Visualization is frequently used for this type of tasks. Starting with a carefully aggregation of the data presented in an overview representation, the quest for information is supported by carefully crafted interactions. These allow filtering the tremendous amount of data, thus removing the standard behavior data and leaving the potentially suspicious one. The latter can then be investigated on increasingly finer levels. Supporting this goal-oriented analysis, we propose novel interactive visualizations implemented in the tool SyCaT-Vis. SyCaT-Vis fosters obtaining important insights into the behavior of computer systems, the processes executed, and the system call sequences issued.},
	keywords = {behavior analysis, security analysis, Security visualization, system call traces},
	doi = {https://doi.org/10.24132/CSRN.2019.2901.1.6},
	url = {http://wscg.zcu.cz/wscg2019/2019-papers/!!_CSRN-2801-6.pdf},
	author = {Alrik Hausdorf and Nicole Hinzmann and Dirk Zeckzer}
}
