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Genomic Sequence Classification Using Probabilistic Topic Modeling

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

Abstract

Taxonomic classification of genomic sequences is usually based on evolutionary distance obtained by alignment. In this work we introduce a novel alignment-free classification approach based on probabilistic topic modeling. Using a k-mer (small fragments of length k) decomposition of DNA sequences and the Latent Dirichlet Allocation algorithm, we built a classifier for 16S rRNA bacterial gene sequences. We tested our method with a tenfold cross validation procedure considering a bacteria dataset of 3000 elements belonging to the most numerous bacteria phyla: Actinobacteria, Firmicutes and Proteobacteria. Experiments were carried out using complete and 400 bp long 16S sequences, in order to test the robustness of the proposed methodology. Our results, in terms of precision scores and for different number of topics, ranges from 100 %, at class level, to 77 % at genus level, for both full and 400 bp length, considering k-mers of length 8. These results demonstrate the effectiveness of the proposed approach.

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Authors and Affiliations

  1. ICAR-CNR, National Research Council of Italy, via P. Castellino 111, 80131, Napoli, Italy

    Massimo La Rosa, Antonino Fiannaca, Riccardo Rizzo & Alfonso Urso

Authors
  1. Massimo La Rosa
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  2. Antonino Fiannaca
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  3. Riccardo Rizzo
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  4. Alfonso Urso
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Corresponding author

Correspondence to Massimo La Rosa .

Editor information

Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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La Rosa, M., Fiannaca, A., Rizzo, R., Urso, A. (2014). Genomic Sequence Classification Using Probabilistic Topic Modeling. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-09042-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09041-2

  • Online ISBN: 978-3-319-09042-9

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