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With the rapid growth of Next Generation Sequencing (NGS) technologies, largeamounts of "omics" data are daily collected and need to be processed. Indexingand compressing large sequences datasets are some of the most important tasksin this context. Here we propose algorithms for the computation of BurrowsWheeler transform relying on Big Data technologies, i.e., Apache Spark andHadoop. Our algorithms are the first ones that distribute the index computationand not only the input dataset, allowing to fully benefit of the availablecloud resources.

Burrows Wheeler Transform on a Large Scale: Algorithms Implemented in Apache Spark