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Data structures based on k-mers for querying large collections of sequencing datasets

View ORCID ProfileCamille Marchet, Christina Boucher, Simon J Puglisi, Paul Medvedev, Mikaël Salson, Rayan Chikhi
doi: https://doi.org/10.1101/866756
Camille Marchet
1Université de Lille, CNRS, CRIStAL UMR 9189, Lille, France
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  • ORCID record for Camille Marchet
  • For correspondence: camille.marchet@univ-lille.fr
Christina Boucher
2Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
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Simon J Puglisi
3Department of Computer Science, Helsinki Institute for Information Technology HIIT, FI-00014 University of Helsinki, Helsinki, Finland
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Paul Medvedev
4Department of Computer Science, The Pennsylvania State University, University Park, USA
5Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, USA
6Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, USA
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Mikaël Salson
1Université de Lille, CNRS, CRIStAL UMR 9189, Lille, France
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Rayan Chikhi
7Institut Pasteur & CNRS, C3BI USR 3756, Paris, France
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Abstract

High-throughput sequencing datasets are usually deposited in public repositories, e.g. the European Nucleotide Archive, to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow to perform online sequence searches; yet such a feature would be highly useful to investigators. Towards this goal, in the last few years several computational approaches have been introduced to index and query large collections of datasets. Here we propose an accessible survey of these approaches, which are generally based on representing datasets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Text and figures have been subsequently improved for clarity.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 17, 2020.
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Data structures based on k-mers for querying large collections of sequencing datasets
Camille Marchet, Christina Boucher, Simon J Puglisi, Paul Medvedev, Mikaël Salson, Rayan Chikhi
bioRxiv 866756; doi: https://doi.org/10.1101/866756
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Data structures based on k-mers for querying large collections of sequencing datasets
Camille Marchet, Christina Boucher, Simon J Puglisi, Paul Medvedev, Mikaël Salson, Rayan Chikhi
bioRxiv 866756; doi: https://doi.org/10.1101/866756

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