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Efficient Storage and Analysis of Genome Data in Databases

Sebastian Dorok; Sebastian Breß; Jens Teubner; Horstfried Läpple; Gunter Saake; Volker Markl
In: 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS). GI-Fachtagungen, Datenbanksysteme für Business, Technologie und Web (BTW 2017), March 6-10, Stuttgart, Germany, Pages 423-442, Lecture Notes in Informatics - Proceedings (LNI), ISBN 978-3-88579-659-6, Gesellschaft für Informatik e.V. (GI), 2017.


Genome-analysis enables researchers to detect mutations within genomes and deduce their consequences. Researchers need reliable analysis platforms to ensure reproducible and comprehensive analysis results. Database systems provide vital support to implement the required sustainable procedures. Nevertheless, they are not used throughout the complete genome-analysis process, because (1) database systems suffer from high storage overhead for genome data and (2) they introduce overhead during domain-specific analysis. To overcome these limitations, we integrate genome-specific compression into database systems using a specialized database schema. Thus, we can reduce the storage overhead to 30%. Moreover, we can exploit genome-data characteristics during query processing allowing us to analyze real-world data sets up to five times faster than specialized analysis tools and eight times faster than a straightforward database approach.