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Georg Tremmel, Atsushi Niida, Satoru Miyano (Human Genome, Center Institute of Medical Science University of Tokyo 4-6-1 Shirokanedai, Minatoku, Tokyo 108-8639, Japan)Reorderable matrices or cluster heat maps have been used to display similarities and differences in complex data sets, especially for the visualization of DNA microarrays. With the advent of High Throughput Methods and Next Generation Sequencing (NGS) the amount of biological raw data available rose dramatically, creating a need for novel ways of visualizing complex data. Here we present an extension of the reorderable matrix - or clustered heatmap - to the 3rd dimension, along with an application that allows for the reordering, visulization, interaction, exploration and analysis of the biological data. 2D Clustered Heat Maps typically present the type of tissue in one dimension and the expression level in the other; by grouping the data through bi-clustering, patterns of expression emerge and become easily distinguishable. By integrating another dimension of data (methylation, etc) and sorting through tri-clustering, we can observe higher dimensional patterns emerge, which can be further explored and analysed in the real-time.