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 consumption of a database approach by up to 35%. Moreover, we 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.
@article{pub9215,
author = {
Dorok, Sebastian
and
Breß, Sebastian
and
Teubner, Jens
and
Läpple, Horstfried
and
Saake, Gunter
and
Markl, Volker
},
title = {Efficiently Storing and Analyzing Genome Data in Database Systems},
year = {2017},
volume = {17},
number = {2},
pages = {139--154},
journal = {Datenbank-Spektrum},
publisher = {Springer}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
