  Indexing sequence data is important in the context of Precision Medicine,
where large amounts of ``omics'' data have to be daily collected and analyzed
in order to categorize patients and identify the most effective therapies. Here
we propose an algorithm for the computation of Burrows Wheeler transform
relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our approach
is the first that distributes the index computation and not only the input
dataset, allowing to fully benefit of the available cloud resources.
