TU/e – COMBat: Visualizing co-occurrence of annotation terms

Remko van Brakel, Mark Fiers, Christof Francke, Michel Westenberg, Huub van de Wetering

We propose a visual analysis approach that employs a matrix-based visualization technique to explore relations between annotation terms in biological data sets. Our flexible framework provides various ways to form combinations of data elements, which results in a co-occurrence matrix. Each cell in this matrix stores a list of items associated with the combination of the corresponding row and column element. By re-arranging the rows and columns of this matrix, and color-coding the cell contents, patterns become visible. Our prototype tool COMBat allows users to construct a new matrix on the fly by selecting subsets of items of interest, or filtering out uninteresting ones, and it provides various additional interaction techniques. We illustrate our approach with a few case studies concerning the identification of functional links between the presence of particular genes or genomic sequences and particular cellular processes.

COMBat: Visualizing co-occurrence of annotation terms
COMBat: Visualizing co-occurrence of annotation terms

Published in: Biological Data Visualization (BioVis), 2013 IEEE Symposium on

Recording of the COMBat presentation at BioVis 2013 by Michel Westenberg.

TU/e – Genome visualisation (Master Thesis)

Graduation project at the Visualization Group of Eindhoven University of Technology, with the goal add genome size data support to the existing DNAVis2 sequence browser. The basic DNAVis2 sequence browser is an OpenGL accelerated visualization tool to visualize and explore a small number of annotated DNA sequences. The application is written in Java using the NetBeans framework and the JOGL OpenGL bindings.

Genome visualization

The extended version is designed and implemented during this master project and adds the data structures and visualizations needed to visualize and explore the data of a complete genome, consisting of 10.000-100.000 annotated DNA sequences. Two approaches are used to provide more inside into the dataset as a whole.

Chromosome visualization

The first approach makes use of the higher level DNA structures to visualize the data distribution and to provide a high level interface to lower level annotations and sequencing data. This provides the biologist with the tools to browse and create sub-selection using the DNA’s structural properties.

BIN/BAC visualization

BIN/BAC visualization (Phi Ball)

The second approach provides a versatile interface to cross-reference various data properties across all abstraction levels. This tool provides the biologist with an inside into the annotation data of the dataset as a whole (or a predefined sub-selection of the data), this results cross-reference visualizations than can contain more then 10.000 row and column items.

Cross-reference visualizations (Unsorted)

Cross-reference visualizations

Cross-reference visualizations (Unsorted)

Cross-reference visualizations (Sorted)

TU/e – Eyes tracking (OpenCV)

The face and pupil recognition application is the result of a computer graphics class, this application makes use of C99/C++ and the OpenCV (Open Source Computer Vision) library to find the face of a user and track his pupils using an inexpensive webcam. The idea it to calculate the viewing position on the computer screen using the webcam images. The resulting application was able to calculate a raw approximation of the screen location after small calibration procedure that uses 5 points on the screen (center, and all four corners). The application is also able to locate the user’s nostrils and mouth.

Eyes tracking

TU/e – Flow visualisation (OpenGL)

The flow visualisation application is the result of a visualization master class, this application uses a number of visualization techniques that are implemented using (OpenGL and C99/C++) to simulate smoke flow in infinite space. This application can visualize the smoke density, particle flows, and pressure areas resulting for a dynamically created smoke source.

Smoke flow visualization (partical trail)

Smoke flow visualization (noise)

Smoke flow visualization (density)

Smoke flow visualization (vector)