  With the rapid growth of Next Generation Sequencing (NGS) technologies, large
amounts of "omics" data are daily collected and need to be processed. Indexing
and compressing large sequences datasets are some of the most important tasks
in this context. Here we propose algorithms for the computation of Burrows
Wheeler transform relying on Big Data technologies, i.e., Apache Spark and
Hadoop. Our algorithms are the first ones that distribute the index computation
and not only the input dataset, allowing to fully benefit of the available
cloud resources.
