Efficiently Storing and Analyzing Genome Data in Database Systems

Abstract

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.

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Notes

  1. 1.

    For simplicity, we only consider mismatching bases and omit inserted or deleted bases.

  2. 2.

    Using the base-centric database schema, we already apply CIGAR operations to the base values of reads.

  3. 3.

    We have to subtract a possible offset if the index of interest is encoded within the fill word.

  4. 4.

    data is available at ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG00096/

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Acknowledgements

The work has received funding from the German Research Foundation (DFG), Collaborative Research Center SFB 876, project C5, from the European Union’s Horizon2020 Research & Innovation Program under grant agreement 671500 (project SAGE), and by the German Ministry for Education and Research as Berlin Big Data Center BBDC (01IS14013A).

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Correspondence to Sebastian Dorok.

Additional information

This is an extended version of our earlier work [15].

Work by S. Dorok was done in part when employed at Bayer Business Services GmbH and Bayer Pharma AG.

Work by S. Breß was done in part when employed at TU Dortmund.

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Dorok, S., Breß, S., Teubner, J. et al. Efficiently Storing and Analyzing Genome Data in Database Systems. Datenbank Spektrum 17, 139–154 (2017). https://doi.org/10.1007/s13222-017-0254-9

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Keywords

  • Main-memory database systems
  • Genome analysis
  • Variant calling