A Big Data Approach for Sequences Indexing on the Cloud via Burrows Wheeler Transform


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.

Proceedings of the First International AAI4H - Advances in Artificial Intelligence for Healthcare Workshop co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020)
Simona E. Rombo
Associate professor

Researcher on Bioinformatics, Network Analysis, Big Data