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Genome sequence analysis with MonetDB

A case study on Ebola virus diversity

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Abstract

Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus

genomes.

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Notes

  1. https://www.monetdb.org/

  2. https://www.monetdb.org/bam/

  3. For this use case, we do not benefit from the read oriented storage that MonetDB/BAM uses. However, [2] shows many use cases for which it does.

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Authors and Affiliations

  1. MonetDB Solutions, Amsterdam, The Netherlands

    Robin Cijvat, Martin Kersten & Ying Zhang

  2. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

    Stefan Manegold, Martin Kersten, Gunnar W. Klau, Alexander Schönhuth & Ying Zhang

  3. Saarland University & Max Planck Institute for Informatics, Saarbrücken, Germany

    Tobias Marschall

Authors
  1. Robin Cijvat
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  2. Stefan Manegold
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  3. Martin Kersten
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  4. Gunnar W. Klau
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  5. Alexander Schönhuth
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  6. Tobias Marschall
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  7. Ying Zhang
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Correspondence to Robin Cijvat.

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Cijvat, R., Manegold, S., Kersten, M. et al. Genome sequence analysis with MonetDB. Datenbank Spektrum 15, 185–191 (2015). https://doi.org/10.1007/s13222-015-0198-x

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  • DOI: https://doi.org/10.1007/s13222-015-0198-x

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