Not known

expVIP is an expression visualization and integration platform, which allows easy analysis of RNA-seq data combined with an intuitive and interactive interface. Users can analyze public and user-specified data sets with minimal bioinformatics knowledge using the expVIP virtual machine. This generates a custom Web browser to visualize, sort, and filter the RNA-seq data and provides outputs for differential gene expression analysis.

Pseudoviewer is an XML web service and web application program for visualizing RNA secondary structures with pseudoknots. Experimental results show that the PseudoViewer web service and web application are useful for resolving many problems with incompatible software components as well as for visualizing large-scale RNA secondary structures with pseudoknots of any type.

MolViewer is a program for visualizing and modifying small molecules under NeXTStep. It is capable of constructing peptides from scratch (given a sequence). It has many display options and can be used to rotate bonds, dihedrals, etc. It will even do some simple energy minimization.

Multiresolution Correlation Analysis (MCA) is a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network. MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations. MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.

MicrobiVis is a tool for visual exploration and interactive analysis of micro-biomic populations. MicrobiVis has been designed in close collaboration with end users. It extends previous interactive systems for explorative dimensionality reduction by including a range of domain relevant features. It contributes a flexible and explorative dimensionality reduction as well as a visual and interactive environment for examination of data subsets. By combining information visualization and methods based on analytic tasks common in microbiology as a means for gaining new and relevant insights.

This is a novel system that enables interactive comparative visualization and analysis of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). This system allows the user to produce, and interactively explore, visualizations of multiple GCxGC-MS data sets, thereby allowing a user to discover differences and features in real time. This system provides statistical support in the form of mean and standard deviation calculations to aid users in identifying meaningful differences between sample groups. We combine these with multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery.

This SDM is based on dense registrations of the input shapes. For a valuable exploration of the shape space in the setting of biological morphometrics two prominent objectives for visual investigation have been identified. The first objective is to detect possible shape variations between anatomically different groups of individuals. The second is to integrate and exploit expert knowledge about relevant regions on the shapes. The first objective can be achieved through the use of dimensionality reduction methods combined with a parameterization defined on user specified classifications. This idea was already successfully applied in data-driven reflectance models and also turns out to be valuable in the context of biological morphometry, as it allows for intuitive exploration of shape variations. The second objective can be achieved by an appropriate weighted linear analysis which delivers a better approximation of shape variations in local neighbourhoods of a user defined region of interest. The methods were applied to real-world biological datasets of rodent mandibles and validated in cooperation with the MPI for Evolutionary Biology. This interactive dynamic visualization of the shape space is based on a custom GPU raycaster.

Next generation sequencing (NGS) technologies are about to revolutionize biological research. Being able to sequence large amounts of DNA or, indirectly, RNA sequences in a short time period opens numerous new possibilities. However, analyzing the large amounts of data generated in NGS is a serious challenge, which requires novel data analysis and visualization methods to allow the biological experimenter to understand the results. This new system deals with the flood of data generated by transcriptome sequencing (RNA-seq) using NGS and it allows the analyzer to get a quick overview of the data, as well as interactively explore interesting regions based on the three important parameters: coverage, transcription, and fit. This system supports the NGS analysis in the following respects: (1) Representation of the coverage sequence in a way that no artifacts are introduced. (2) Easy determination of a fit of an open reading frame (ORF) to a transcript by mapping the coverage sequence directly into the ORF representation. (3) Providing automatic support for finding interesting regions to address the problems that the overwhelming volume of data comes with. (4) Providing an overview representation that allows parameter tuning and enables quick access to interesting areas of the genome.

This novel mathematical model describes the stochastic process of the ligand-receptor clustering. To study the structure and the size of the ligand-receptor clusters, a stochastic particle simulation is employed. Besides the translation of the particles on the cellular membrane, the particle rotation is taken into account as binding sites are explicitly modelled. Glyph-based visualization techniques are used to validate and analyze the results of our in-silico model. Information on the individual clusters as well as particle-specific data can be selected by the user and is mapped to colors to highlight certain properties of the data. The visualization supports the process of model development by visual data analysis including the identification of cluster components as well as the illustration of particle trajectories.

Scifer is a visualization software designed for the interactive analysis of scientific data.

Scifer currently supports the following application areas:

• Biology: 3D video microscopy, TEM/SEM records of tissue
• Climate research: time series analysis
• Computational fluid dynamics: time series and feature analysis
• Graph analysis
• Multidimensional data analysis

This visualization method combines similarity measures of trajectory shapes and movement-relevant parameters to account for biological questions. In an interactive frame-work, the user can first cluster the cell trajectories according to problem-specific features and interactively validate and analyze the resulting clustering. The flexibility of the similarity metric constitutes a vital feature of this technique. It can be adjusted to describe a large variety of trajectory characteristics and may therefore be adapted to a large variety of task-specific demands. A second feature that proved highly useful is the extension of the visualization to 3D. While most existing techniques only plot the cell lineages in 2D, this method provides the user with different techniques to investigate their data in the natural 3D space.