Actively maintained

The Interactive Genomics Patient Stratification explorer (iGPSe) significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. This system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, it is possible to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction.

inPHAP is an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP's ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci.

SketchBio aims to provide a rapid-to-use and easy-to-learn 3D modeling tool for biologists to enable effective interactive 3D “what if” scenario exploration for the exploration of subcellular structures.

It includes three novel features: crystal by example, pose-mode physics, and spring-based layout that accelerate operations common in the formation of molecular models. 

RuleBender is a novel visualization system for the integrated visualization, modeling and simulation of rule-based intra-cellular biochemistry. Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks.

This is a novel visual analytics system called GenAMap. GenAMap replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information. We present the results of a preliminary evaluation that validated our basic approach.

LayerCake is a tool designed to assist in the exploration of the genetic variability of the population of viruses at multiple time points and in multiple individuals, a task that necessitates considering large amounts of sequence data and the quality issues inherent in obtaining such data in a practical manner. This design affords the examination of the amount of variability and mutation at each position in the genome for many populations of viruses. This design contains novel visualization techniques that support this specific class of analysis while addressing the issues of data aggregation, confidence visualization, and interaction support that arise when making use of large amounts of sequence data with variable uncertainty. These techniques generalize to a wide class of visualization problems where confidence is not known a priori, and aggregation in multiple directions is necessary.

This is a multi-scale visualization method, including a novel tree layout that both shows population status over time and can easily scale to very large populations. From this layout, the user can navigate to visualizations for moments in time or for individual entities. We demonstrate the effectiveness of the visualization on an existing evolutionary simulation called EVE: Evolution in Variable Environments.

We present HiTSEE (High-Throughput Screening Exploration Environment) a visualization tool for the analysis of large chemical screens for the analysis of biochemical processes. The tool supports the analysis of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach based on the projection of one or few molecules of interest and the expansion around their neighborhood allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies.

The iHAT prototype tool supports the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Moreover, data-type dependent colormaps and aggregation strategies as well as different filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT is aimed at exploiting the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. Together with its interactive features and a database backend for fast data retrieval, iHAT is a prototype for a visual analytics system for genome-wide association studies.

See the project page for further details.

The proliferation of next-generation sequencing (NGS) technologies and analysis tools present new challenges to genome browsers. These challenges include supporting very large datasets, integrating analysis tools with data visualization to help reason about and improve analyses, and sharing or publishing fully interactive visualizations. The Galaxy Track Browser (GTB) is a Web-based genome browser integrated into the Galaxy platform that addresses these challenges. GTB is the first Web-based genome browser to provide a full multi-resolution data model; this model supports efficient data retrieval from very large datasets. GTB leverages the Galaxy platform to combine data visualization and data analysis; users can specify parameter values and run tools to produce new data, all within GTB. GTB also provides interactive filters that dynamically show and hide data and can be used to identify data for further investigation. GTB is available on every Galaxy server, and visualizations can be created for both standard and custom genome builds. Fully interactive GTB visualizations can be shared with colleagues and published on the Web using a simple graphical user interface.