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Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction

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  • Daniel Gamermann
  • Arnau Montagud
  • J Alberto Conejero
  • Pedro Fernández de Córdoba
  • Javier F Urchueguía

Abstract

Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of genes) results in distinct inferred dendrograms. In this work, we evaluate differences between dendrograms reconstructed with different methodologies and for different sets of organisms chosen at random from a much larger set. A statistical analysis is performed to estimate fluctuations between the results obtained from the different methodologies that allows us to validate a systematic approach, based on the comparison of the organisms’ metabolic networks for inferring dendrograms. This has the advantage that it allows the comparison of organisms very far away in the evolutionary tree even if they have no known ortholog gene in common. Our results show that dendrograms built using information from metabolic networks are similar to the standard sequence-based dendrograms and can be a complement to them.

Suggested Citation

  • Daniel Gamermann & Arnau Montagud & J Alberto Conejero & Pedro Fernández de Córdoba & Javier F Urchueguía, 2019. "Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
  • Handle: RePEc:plo:pone00:0221631
    DOI: 10.1371/journal.pone.0221631
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    References listed on IDEAS

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    1. Anonymous, 2013. "Notes from the Editors," American Political Science Review, Cambridge University Press, vol. 107(4), pages 1-1, November.
    2. Anonymous, 2013. "Notes from the Editors," American Political Science Review, Cambridge University Press, vol. 107(3), pages 1-1, August.
    3. Anonymous, 2013. "Notes from the Editors," American Political Science Review, Cambridge University Press, vol. 107(2), pages 1-1, May.
    4. Anonymous, 2013. "Notes from the Editors," American Political Science Review, Cambridge University Press, vol. 107(1), pages 1-1, February.
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    Cited by:

    1. Gamermann, Daniel & Pellizzaro, José Antônio, 2022. "An algorithm for network community structure determination by surprise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

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