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Proposal of a New Bioinformatics Pipeline for Metataxonomics in Precision Medicine

  • Osvaldo Graña-CastroEmail author
  • Hugo López-Fernández
  • Florentino Fdez-Riverola
  • Fátima Al-Shahrour
  • Daniel Glez-Peña
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1005)

Abstract

Microbes are found all over the human body and they have a direct impact on the immune system, metabolism and homeostasis. The homeostatic balance of the intestinal microflora can be broken under certain conditions, a situation known as dysbiosis, which can lead to disease, including certain types of cancer, or even affect a patient response to a therapeutic treatment. Metataxonomics pursues the identification of the bacteria species that are present in biological samples of interest, through the sequencing of the 16S rRNA gene, a highly conserved genetic marker that is present in most prokaryotes. Interactions between the microbiota and the human host are being very relevant in the expansion of precision medicine and cancer research, to better predict the risk of disease and to implement bacteria-directed therapeutics. In order to take metataxonomics to the clinic, efficient bioinformatics pipelines are required, that are flexible and portable, and that are able to classify groups of biological samples according to microbiome diversity. With this objective in mind, we propose a new bioinformatics pipeline to analyze biological samples obtained through NGS of the 16S rRNA gene, doing all the required quality checks and computational calculations. The results obtained with this pipeline are aimed to be interpreted together with host DNA exome or RNA-Seq studies and clinical data, to improve the knowledge about the potential reasons that could lead to disease or to a worst patient treatment response.

Keywords

NGS 16S rRNA gene Metataxonomics Precision medicine 

Notes

Acknowledgments

The SING group thanks the CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was partially supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding ED431C2018/55-GRC Competitive Reference Group, and by the Plataforma de Bioinformática from the Instituto de Salud Carlos III (PT17/0009/0011).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Osvaldo Graña-Castro
    • 1
    • 2
    Email author
  • Hugo López-Fernández
    • 2
    • 3
    • 4
  • Florentino Fdez-Riverola
    • 2
    • 3
    • 4
  • Fátima Al-Shahrour
    • 1
  • Daniel Glez-Peña
    • 2
    • 3
    • 4
  1. 1.Bioinformatics Unit, Structural Biology ProgrammeSpanish National Cancer Research Centre (CNIO)MadridSpain
  2. 2.Department of Computer Science, ESEIUniversity of VigoOurenseSpain
  3. 3.The Biomedical Research Centre (CINBIO)VigoSpain
  4. 4.SING Research GroupGalicia Sur Health Research Institute (ISS Galicia Sur), SERGAS-UVIGOVigoSpain

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