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International Conference on Machine Learning, Optimization, and Data Science

LOD 2021: Machine Learning, Optimization, and Data Science pp 31–44Cite as

A k-mer Based Sequence Similarity for Pangenomic Analyses

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13164))

Abstract

In this work we propose an approach to improve the performance of a current methodology, computing k-mer based sequence similarity via Jaccard index, for pangenomic analyses. Recent studies have shown a good performance of such a measure for retrieving homology among genetic sequences belonging to a group of genomes.

Our improvement is obtained by exploiting a suitable k-mer representation, which enables a fast and memory-cheap computation of sequence similarity. Experimental results on genomes of living organisms of different species give an evidence that a state of the art methodology is here improved, in terms of running time and memory requirements.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Verona, 37134, Verona, Italy

    Vincenzo Bonnici, Andrea Cracco & Giuditta Franco

Authors
  1. Vincenzo Bonnici
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  2. Andrea Cracco
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  3. Giuditta Franco
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Corresponding author

Correspondence to Vincenzo Bonnici .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Bonnici, V., Cracco, A., Franco, G. (2022). A k-mer Based Sequence Similarity for Pangenomic Analyses. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13164. Springer, Cham. https://doi.org/10.1007/978-3-030-95470-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-95470-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95469-7

  • Online ISBN: 978-3-030-95470-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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