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Discovering Trends in Environmental Time-Series with Supervised Classification of Metatranscriptomic Reads and Empirical Mode Decomposition

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Biomedical Engineering Systems and Technologies (BIOSTEC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1024))

Abstract

In metagenomic and metatranscriptomic studies, the assignment of reads to taxonomic bins is typically performed by sequence similarity or phylogeny based approaches. Such methods become less effective if the sequences are closely related and/or of limited length. Here, we propose an approach for multi-class supervised classification of metatranscriptomic reads of short length (100–300 bp) which exploits k-mers frequencies as discriminating features. In addition, we take a first step in addressing the lack of established methods for the analysis of periodic features in environmental time-series by proposing Empirical Mode Decomposition as a way of extracting information on heterogeneity and population dynamics in natural microbial communities. To prove the validity of our computational approach as an effective tool to generate new biological insights, we applied it to investigate the transcriptional dynamics of viral infection in the ocean. We used data extracted from a previously published metatranscriptome profile of a naturally occurring oceanic bacterial assemblage sampled Lagrangially over 3 days. We discovered the existence of light-dark oscillations in the expression patterns of auxiliary metabolic genes in cyanophages which follow the harmonic diel transcription of both oxygenic photoautotrophic and heterotrophic members of the community, in agreement to what other studies have just recently found. Our proposed methodology can be extended to many other datasets opening opportunities for a better understanding of the structure and function of microbial communities in their natural environment.

E. Acerbi and C. Chénard—Contributed equally.

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Acknowledgements

The authors would like to acknowledge financial support from Singapore’s Ministry of Education Academic Research Fund Tier 3 under the research grant MOE2013-T3-1-013, Singapore’s National Research Foundation under its Marine Science Research and Development Programme (Award No. MSRDP-P13) and the Singapore Centre for Environmental Life Sciences Engineering (SCELSE), whose research is supported by the National Research Foundation Singapore, Ministry of Education, Nanyang Technological University and National University of Singapore, under its Research Centre of Excellence Program. The authors would like to thank Fabio Stella, Rohan Williams and James Houghton for their valuable feedbacks.

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Authors and Affiliations

  1. Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Jurong West, Singapore

    Enzo Acerbi, Stephan C. Schuster & Federico M. Lauro

  2. Asian School of the Environment, Nanyang Technological University, Jurong West, Singapore

    Caroline Chénard & Federico M. Lauro

Authors
  1. Enzo Acerbi
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  2. Caroline Chénard
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  3. Stephan C. Schuster
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  4. Federico M. Lauro
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Corresponding author

Correspondence to Federico M. Lauro .

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Editors and Affiliations

  1. University of Sao Paulo, São Paulo, Brazil

    Alberto Cliquet Jr.

  2. University of Saskatchewan, Saskatoon, Canada

    Sheldon Wiebe

  3. College of Charleston, Charleston, SC, USA

    Paul Anderson

  4. University of Rome Tor Vergata, Rome, Italy

    Giovanni Saggio

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

  6. LIBPhys, New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

  7. Instituto de Telecomunicações and Instituto Superior Técnico, Lisbon, Portugal

    Ana Fred

  8. Madeira Interactive Technologies Institute, Universidade da Madeira, Funchal, Portugal

    Sergi Bermúdez i Badia

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Acerbi, E., Chénard, C., Schuster, S.C., Lauro, F.M. (2019). Discovering Trends in Environmental Time-Series with Supervised Classification of Metatranscriptomic Reads and Empirical Mode Decomposition. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-29196-9_11

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