Michael Lesnussa

Visualization in Health Grid Environments: A Novel Service and Business Approach

Advanced visualization technologies are gaining major importance to allow presentation and manipulation of high dimensional data. Since new health technologies are constantly increasing in complexity, adequate information processing is required for diagnostics and treatment. Therefore, the German D-Grid initiative started to build visualization centers in 2008, which have recently been embedded into the existing compute and storage infrastructure. This paper describes an analysis of this infrastructure and the interplay with life science applications for 3D and 4D visualization and manipulation. Furthermore, the performance and business aspects regarding accounting, pricing and billing are investigated. The results show the viability and the opportunities for further optimization of this novel service approach and the possibilities for a sustainable business scenario.

Investigation of the spatial structure and interactions of the genome at sub-kilobase-pair resolution using T2C

Chromosome conformation capture (3C) and its derivatives (e.g., 4C, 5C and Hi-C) are used to analyze the 3D organization of genomes. We recently developed targeted chromatin capture (T2C), an inexpensive method for studying the 3D organization of genomes, interactomes and structural changes associated with gene regulation, the cell cycle, and cell survival and development. Here, we present the protocol for T2C based on capture, describing all experimental steps and bio-informatic tools in full detail. T2C offers high resolution, a large dynamic interaction frequency range and a high signal-to-noise ratio. Its resolution is determined by the resulting fragment size of the chosen restriction enzyme, which can lead to sub-kilobase-pair resolution. T2Cs high coverage allows the identification of the interactome of each individual DNA fragment, which makes binning of reads (often used in other methods) basically unnecessary. Notably, T2C requires low sequencing efforts. T2C also allows multiplexing of samples for the direct comparison of multiple samples. It can be used to study topologically associating domains (TADs), determining their position, shape, boundaries, and intra- and inter-domain interactions, as well as the composition of aggregated loops, interactions between nucleosomes, individual transcription factor binding sites, and promoters and enhancers. T2C can be performed by any investigator with basic skills in molecular biology techniques in ∼7-8 d. Data analysis requires basic expertise in bioinformatics and in Linux and Python environments.