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


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.

Raffaele Giancarlo
Full professor

Professor of Algorithms and PI of the project: Research Unit Univ. Palermo

Simona E. Rombo
Associate professor

Researcher on Bioinformatics, Network Analysis, Big Data