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 'Masakari' 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.

MOTIVATION: RNA secondary structure is a useful representation for studying the function of RNA, which captures most of the free energy of RNA folding. Using empirically determined energy parameters, secondary structures of nucleic acids can be efficiently computed by recursive algorithms. Several software packages supporting this task are readily available. As RNA secondary structures are outerplanar graphs, they can be drawn without intersection in the plane. Interpretation by the practitioner is eased when these drawings conform to a series of additional constraints beyond outerplanarity. These constraints are the reason why RNA drawing is difficult. Many RNA drawing algorithms therefore do not always produce intersection-free (outerplanar) drawings. RESULTS: To remedy this shortcoming we propose here the RNApuzzler algorithm which is guaranteed to produce intersection-free drawings. It is based on a drawing algorithm respecting constraints based on nucleotide distances (RNAturtle). We investigate relaxations of these constraints allowing for intersection-free drawings. Based on these relaxations, we implemented a fully automated, simple, and robust algorithm that produces aesthetic drawings adhering to previously established guidelines. We tested our algorithm using the RFAM database and found that we can compute intersection-free drawings of all RNAs therein efficiently. AVAILABILITY AND IMPLEMENTATION: The software can be accessed freely at: https://github.com/dwiegreffe/RNApuzzler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Histone modifications play an important role in gene regulation. Their genomic locations are of great interest. Usually, the location is measured by ChIP-seq and analyzed with a peak-caller. Replicated ChIP-seq experiments become more and more available. However, their analysis is based on single-experiment peak-calling or on tools like PePr which allows peak-calling of replicates but whose underlying model might not be suitable for the conditions under which the experiments are performed. RESULTS: We propose a new peak-caller called 'Sierra Platinum' that allows peak-calling of replicated ChIP-seq experiments. Moreover, it provides a variety of quality measures together with integrated visualizations supporting the assessment of the replicates and the resulting peaks, as well as steering the peak-calling process. CONCLUSION: We show that Sierra Platinum outperforms currently available methods using a newly generated benchmark data set and using real data from the NIH Roadmap Epigenomics Project. It is robust against noisy replicates.

Combinatorial optimization problems and corresponding (meta-)heuristics have received much attention in the literature. Especially, the structural or topological analysis of search landscapes is important for evaluating the applicability and the performance of search operators for a given problem. However, this analysis is often tedious and usually the focus is on one specific problem and only a few operators. We present a visual analysis method that can be applied to a wide variety of problems and search operators. The method is based on steepest descent walks and shortest distances in the search landscape. The visualization shows the search landscape as seen by the search algorithm. It supports the topological analysis as well as the comparison of search landscapes. We showcase the method by applying it to two different search operators on the TSP, the QAP, and the SMTTP. Our results show how differences between search operators manifest in the search landscapes and how conclusions about the suitability of the search operator for different optimizations can be drawn.

Software engineering tasks like understanding of (legacy) software, checking guidelines, finding structure, or re-engineering of existing software require the analysis of the static software structure. The optimal visualization of this structure depends on the task at hand. In general, the software structure is mapped to a graph and graph drawing algorithms are used for displaying the structure. The task of drawing these graphs becomes more involved if not only one type of relations, e.g., call graphs, but many relation types should be analyzed at the same time. We propose a small-multiples node-link visualization, where each small visualization shows the graph related to one edge type. The visualizations are synchronized using selection and coordinated views. Using a separate view for each of the relations provides additional analysis capabilities. The main advantage of this approach is, that optimized drawing algorithms can be used for each type of relationship while being able to display multiple relationships at the same time.

Providing sustainable urban water distribution systems is a complex process involving decision makers and authorities as well as the public opinion. In this process, a successful visualization should support analyzing and comparing different scenarios. Therefore, in this study, several visualization techniques are combined to provide an effective representation of a water distribution system. The main parameter to visualize is nodal pressure. First, the nodal pressure values for the study area are obtained as a function of operation time using the U.S. Environmental Protection Agency EPANET model. Then, the time steps are represented as small multiple maps. In order to improve the visibility of each node, all the nodes along the edges are aggregated and represented as a single node. The circle view representation is used to visualize two values of a nodal pressure variable. Finally, the water network is mapped onto a grid, which allows a user to see every node individually without overlaps. Results show that this combination of visualization techniques to illustrate the water network scenarios is beneficial for decision makers to provide sustainable urban water services.

Software systems are often modeled and visualized as graphs in order to understand their higher-level structure: code entities are connected by dependencies or couplings. However, when only considering one type of code coupling such as method calls, the understanding gained stays limited to this specific aspect. Encoding multiple types of code coupling in the graph promises to broaden the understanding. Different approaches already exist for visually discerning those types in graph diagrams. In this paper, we study two of these techniquesa node-link and a matrix approach-in a realistic scenario where the classes and interfaces of a system are connected by six different types of code coupling. The explorative user study that we conducted with interactive versions of the two visualizations focuses on getting an insight on how software developers use the visualizations for understanding an unknown system. We classified typical visual structures that the participants were able to identify and connected these structures to software engineering problems. Despite the fundamental difference in approach, the participants identified the same graph structures targeting similar tasks with both visualizations.

Many visualization techniques are used by software engineers to understand and to analyze the static structure of software systems, with the static structure being extracted from the source code. However, the need for scalable visualizations, which take into account the increasing number of code entities (classes and interfaces) and different types of code couplings (relations between them) in software systems, is still not sufficiently fulfilled. In this paper, we extend the design of an existing multi-matrix visualization approach to represent the static structure of software systems in a scalable way. First, we extended the data model and the algorithms. Second, we added more visualization and interaction elements. Finally, we incorporated the folding (collapsing) and the unfolding (expanding) of the package hierarchy, which have quadratic time complexity and quadratic space complexity in the number of nodes. This extended approach can be applied to support program comprehension and to analyze the static structure of software systems.

Abstract Particle swarm optimization (PSO) is a metaheuristic that has been applied successfully to many continuous and combinatorial optimization problems, e.g., in the fields of economics, engineering, and natural sciences. In PSO, a swarm of particles moves within a search space in order to find an optimal solution. Unfortunately, it is hard to understand in detail why and how changes in the design of PSO algorithms affect the optimization behavior. Visualizing the particle states could provide substantially better insight into PSO algorithms. Though in case of combinatorial optimization problems, it often raises the problem of illustrating the states within the discrete search space that cannot be embedded spatially. We propose a visualization approach to depict the optimization problem topologically using a landscape metaphor. This visualization is augmented by an illustration of the time‐dependent states of the particles. Thus, the user of dPSO‐Vis is able to analyze the swarm's behavior within the search space. In principle, our method can be used for any optimization algorithm where a swarm of individuals searches within a discrete search space. Our approach is verified with a case study for the PSO algorithm HelixPSO that predicts the secondary structure of RNA molecules.

This paper presents a case study for identifying security-safety requirements by using safety analysis techniques. In order to construct distributed software-intensive safety-critical systems, it is crucial to identify not only the safety requirements, but also the security requirements simultaneously, due to the fact that security attacks on the communication channels could cause safety consequences, such as damage to properties or even loss of human lives. Security-safety requirements are proposed for addressing the requirement that describes the safety, security requirements and the influence of the security attacks on safety requirements. Although many safety and security analyses techniques are available for deriving the security-safety requirements separately, no effective approach exists that uses an identical model for identifying integrated security and safety requirements. In this paper, the procedures and lessons learned for deriving integrated security-safety requirements using one identical model that is established by safety analysis techniques are presented. The results from this case study show that the safety analysis techniques are applicable for determining integrated requirements for describing the behavior of an outdoor robot.

The automotive industry demands visual support for the verification of the quality of their products from the design phase to the manufacturing phase. This implies the need of tools for measurement planning, programming measuring devices, managing measurement data, and the visual exploration of the measurement results. To improve the quality control throughout the whole process chain an integration of such tools in a platform independent framework is crucial. We present eMMA (enhanced Measure Management Application), a client/server system integrating measurement planning, data management, and straightforward as well as sophisticated visual exploration tools in a single framework.