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ND-GiST: A Novel Method for Disk-Resident k-mer Indexing

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New Knowledge in Information Systems and Technologies (WorldCIST'19 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 931))

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Abstract

Several challenges are related to metagenomics, one of which is the data management. A related central concept is k-mer which means a possible subsequence of length k from a DNA (sub)sequence. In this work, the focus is on indexing k-mers and supporting box queries where a query string of length k might have multiple allowed nucleobases per position. A novel index structure: ND-GiST is introduced which has capability to handle box queries. Comparing it with full table scan and the traditional B-tree, the performance results of ND-GiST are encouraging.

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Notes

  1. 1.

    See: https://www.postgresql.org/docs/10/gist.html.

  2. 2.

    The records are listed in the order of insertion into the tree.

  3. 3.

    https://www.bioinformatics.org/sms/iupac.html.

  4. 4.

    See: https://www.postgresql.org/docs/10/indexes-types.html.

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Acknowledgments

The project has been supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 643476 (COMPARE), by the Novo Nordisk Foundation Interdisciplinary Synergy Programme [Grant NNF15OC0016584] and by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002).

Author information

Authors and Affiliations

  1. Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary

    János Márk Szalai-Gindl, Attila Kiss & Gábor Halász

  2. Department of Physics of Complex Systems, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest, 1117, Hungary

    László Dobos & István Csabai

Authors
  1. János Márk Szalai-Gindl
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  2. Attila Kiss
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  3. Gábor Halász
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  4. László Dobos
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  5. István Csabai
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Corresponding author

Correspondence to János Márk Szalai-Gindl .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Faculdade de Engenharia/LIACC, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Szalai-Gindl, J.M., Kiss, A., Halász, G., Dobos, L., Csabai, I. (2019). ND-GiST: A Novel Method for Disk-Resident k-mer Indexing. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_63

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