SUMMARY: Current synteny visualization tools either focus on small regions of sequence and do not illustrate genome-wide trends, or are complicated to use and create visualizations that are difficult to interpret. To address this challenge, The Comparative Genomics Platform (CoGe) has developed two web-based tools to visualize synteny across whole genomes. SynMap2 and SynMap3D allow researchers to explore whole genome synteny patterns (across two or three genomes, respectively) in responsive, web-based visualization and virtual reality environments. Both tools have access to the extensive CoGe genome database (containing over 30 000 genomes) as well as the option for users to upload their own data. By leveraging modern web technologies there is no installation required, making the tools widely accessible and easy to use. AVAILABILITY AND IMPLEMENTATION: Both tools are open source (MIT license) and freely available for use online through CoGe ( https://genomevolution.org ). SynMap2 and SynMap3D can be accessed at http://genomevolution.org/coge/SynMap.pl and http://genomevolution.org/coge/SynMap3D.pl , respectively. Source code is available: https://github.com/LyonsLab/coge . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions.