SC19 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Poster 67: Genie: an MPEG-G Conformant Software to Compress Genomic Data.

Authors: Brian E. Bliss (University of Illinois), Joshua M. Allen (University of Illinois), Saurabh Baheti (Mayo Clinic), Matthew A. Bockol (Mayo Clinic), Shubham Chandak (Stanford University), Jaime Delgado (Polytechnic University of Catalonia), Jan Fostier (Ghent University), Josep L. Gelpi (University of Barcelona), Steven N. Hart (Mayo Clinic), Mikel Hernaez Arrazola (University of Illinois), Matthew E. Hudson (University of Illinois), Michael T. Kalmbach (Mayo Clinic), Eric W. Klee (Mayo Clinic), Liudmila S. Mainzer (University of Illinois), Fabian Müntefering (Leibniz University), Daniel Naro (Barcelona Supercomputing Center), Idoia Ochoa-Alvarez (University of Illinois), Jörn Ostermann (Leibniz University), Tom Paridaens (Ghent University), Christian A. Ross (Mayo Clinic), Jan Voges (Leibniz University), Eric D. Wieben (Mayo Clinic), Mingyu Yang (University of Illinois), Tsachy Weissman (Stanford University), Mathieu Wiepert (Mayo Clinic)

Abstract: Precision medicine has unprecedented potential for accurate diagnosis and effective treatment. It is supported by an explosion of genomic data, which continues to accumulate at accelerated pace. Yet storage and analysis of petascale genomic data is expensive, and that cost will ultimately be borne by the patients and citizens. The Moving Picture Experts Group (MPEG) has developed MPEG-G, a new open standard to compress, store, transmit and process genomic sequencing data that provides an evolved and superior alternative to currently used genomic file formats. Our poster will showcase software package GENIE, the first open source implementation of an encoder-decoder pair that is compliant with the MPEG-G specifications and delivers all its benefits: efficient compression, selective access, transport and analysis, guarantee of long-term support, and embedded mechanisms for annotation and encryption of compressed information. GENIE will create a step-change in medical genomics by reducing the cost of data storage and analysis.

Best Poster Finalist (BP): no

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