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A big data approach for sequences indexing on the cloud via burrows wheeler transform
Authors
Mario Randazzo
Simona Ester Rombo
Publication date
20 July 2020
Publisher
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on
arXiv
Abstract
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. Copyright © 2020 for this paper by its authors
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Archivio istituzionale della ricerca - Università di Palermo
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oai:iris.unipa.it:10447/528783
Last time updated on 16/03/2022