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Motivation: In this paper we present the latest release of EBIC, anext-generation biclustering algorithm for mining genetic data. The majorcontribution of this paper is adding support for big data, making it possibleto efficiently run large genomic data mining analyses. Additional enhancementsinclude integration with R and Bioconductor and an option to remove influenceof missing value on the final result.  Results: EBIC was applied to datasets of different sizes, including a largeDNA methylation dataset with 436,444 rows. For the largest dataset we observedover 6.6 fold speedup in computation time on a cluster of 8 GPUs compared torunning the method on a single GPU. This proves high scalability of thealgorithm.  Availability: The latest version of EBIC could be downloaded fromhttp://github.com/EpistasisLab/ebic . Installation and usage instructions arealso available online.

EBIC: an open source software for high-dimensional and big data biclustering analyses