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)


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.


NGS 16S rRNA gene Metataxonomics Precision medicine 



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).


  1. 1.
    Turnbaugh, P.J., Ley, R.E., Hamady, M., Fraser-Liggett, C.M., Knight, R., Gordon, J.I.: The human microbiome project. Nature 449, 804–810 (2007)CrossRefGoogle Scholar
  2. 2.
    Sender, R., Fuchs, S., Milo, R.: Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340 (2016)CrossRefGoogle Scholar
  3. 3.
    Gilbert, J.A., Blaser, M.J., Caporaso, J.G., Jansson, J.K., Lynch, S.V., Knight, R.: Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018)CrossRefGoogle Scholar
  4. 4.
    Sender, R., Fuchs, S., Milo, R.: Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14, e1002533 (2016)CrossRefGoogle Scholar
  5. 5.
    Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., Narasimhan, G.: Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evol. Bioinform. Online 12, 5–16 (2016)Google Scholar
  6. 6.
    Lloyd-Price, J., Abu-Ali, G., Huttenhower, C.: The healthy human microbiome. Genome Med. 8, 51 (2016)CrossRefGoogle Scholar
  7. 7.
    Kim, B.-S., Jeon, Y.-S., Chun, J.: Current status and future promise of the human microbiome. Pediatr. Gastroenterol. Hepatol. Nutr. 16, 71–79 (2013)CrossRefGoogle Scholar
  8. 8.
    DeGruttola, A.K., Low, D., Mizoguchi, A., Mizoguchi, E.: Current understanding of dysbiosis in disease in human and animal models. Inflamm. Bowel Dis. 22, 1137–1150 (2016)CrossRefGoogle Scholar
  9. 9.
    Banerjee, J., Mishra, N., Dhas, Y.: Metagenomics: a new horizon in cancer research. Meta Gene 5, 84–89 (2015)CrossRefGoogle Scholar
  10. 10.
    Bultman, S.J.: Emerging roles of the microbiome in cancer. Carcinogenesis 35, 249–255 (2014)CrossRefGoogle Scholar
  11. 11.
    Buffington, S.A., Di Prisco, G.V., Auchtung, T.A., Ajami, N.J., Petrosino, J.F., Costa-Mattioli, M.: Microbial reconstitution reverses maternal diet-induced social and synaptic deficits in offspring. Cell 165, 1762–1775 (2016)CrossRefGoogle Scholar
  12. 12.
    Gevers, D., Kugathasan, S., Denson, L.A., Vazquez-Baeza, Y., Van Treuren, W., Ren, B., Schwager, E., Knights, D., Song, S.J., Yassour, M., Morgan, X.C., Kostic, A.D., Luo, C., Gonzalez, A., McDonald, D., Haberman, Y., Walters, T., Baker, S., Rosh, J., Stephens, M., Heyman, M., Markowitz, J., Baldassano, R., Griffiths, A., Sylvester, F., Mack, D., Kim, S., Crandall, W., Hyams, J., Huttenhower, C., Knight, R., Xavier, R.J.: The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014)CrossRefGoogle Scholar
  13. 13.
    Petrosino, J.F.: The microbiome in precision medicine: the way forward. Genome Med. 10, 12 (2018)CrossRefGoogle Scholar
  14. 14.
    Gopalakrishnan, V., Spencer, C.N., Nezi, L., Reuben, A., Andrews, M.C., Karpinets, T.V., Prieto, P.A., Vicente, D., Hoffman, K., Wei, S.C., Cogdill, A.P., Zhao, L., Hudgens, C.W., Hutchinson, D.S., Manzo, T., Petaccia de Macedo, M., Cotechini, T., Kumar, T., Chen, W.S., Reddy, S.M., Szczepaniak Sloane, R., Galloway-Pena, J., Jiang, H., Chen, P.L., Shpall, E.J., Rezvani, K., Alousi, A.M., Chemaly, R.F., Shelburne, S., Vence, L.M., Okhuysen, P.C., Jensen, V.B., Swennes, A.G., McAllister, F., Marcelo Riquelme Sanchez, E., Zhang, Y., Le Chatelier, E., Zitvogel, L., Pons, N., Austin-Breneman, J.L., Haydu, L.E., Burton, E.M., Gardner, J.M., Sirmans, E., Hu, J., Lazar, A.J., Tsujikawa, T., Diab, A., Tawbi, H., Glitza, I.C., Hwu, W.J., Patel, S.P., Woodman, S.E., Amaria, R.N., Davies, M.A., Gershenwald, J.E., Hwu, P., Lee, J.E., Zhang, J., Coussens, L.M., Cooper, Z.A., Futreal, P.A., Daniel, C.R., Ajami, N.J., Petrosino, J.F., Tetzlaff, M.T., Sharma, P., Allison, J.P., Jenq, R.R., Wargo, J.A.: Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018)Google Scholar
  15. 15.
    Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.-L., Luke, J.J., Gajewski, T.F.: The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018)CrossRefGoogle Scholar
  16. 16.
    Routy, B., Le Chatelier, E., Derosa, L., Duong, C.P.M., Alou, M.T., Daillere, R., Fluckiger, A., Messaoudene, M., Rauber, C., Roberti, M.P., Fidelle, M., Flament, C., Poirier-Colame, V., Opolon, P., Klein, C., Iribarren, K., Mondragon, L., Jacquelot, N., Qu, B., Ferrere, G., Clemenson, C., Mezquita, L., Masip, J.R., Naltet, C., Brosseau, S., Kaderbhai, C., Richard, C., Rizvi, H., Levenez, F., Galleron, N., Quinquis, B., Pons, N., Ryffel, B., Minard-Colin, V., Gonin, P., Soria, J.-C., Deutsch, E., Loriot, Y., Ghiringhelli, F., Zalcman, G., Goldwasser, F., Escudier, B., Hellmann, M.D., Eggermont, A., Raoult, D., Albiges, L., Kroemer, G., Zitvogel, L.: Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018)CrossRefGoogle Scholar
  17. 17.
    Zhu, W., Winter, M.G., Byndloss, M.X., Spiga, L., Duerkop, B.A., Hughes, E.R., Buttner, L., de Lima Romao, E., Behrendt, C.L., Lopez, C.A., Sifuentes-Dominguez, L., Huff-Hardy, K., Wilson, R.P., Gillis, C.C., Tukel, C., Koh, A.Y., Burstein, E., Hooper, L.V., Baumler, A.J., Winter, S.E.: Precision editing of the gut microbiota ameliorates colitis. Nature 553, 208–211 (2018)CrossRefGoogle Scholar
  18. 18.
    Winter, S.E., Winter, M.G., Xavier, M.N., Thiennimitr, P., Poon, V., Keestra, A.M., Laughlin, R.C., Gomez, G., Wu, J., Lawhon, S.D., Popova, I.E., Parikh, S.J., Adams, L.G., Tsolis, R.M., Stewart, V.J., Baumler, A.J.: Host-derived nitrate boosts growth of E. coli in the inflamed gut. Science 339, 708–711 (2013)CrossRefGoogle Scholar
  19. 19.
    Spanogiannopoulos, P., Bess, E.N., Carmody, R.N., Turnbaugh, P.J.: The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14, 273–287 (2016)CrossRefGoogle Scholar
  20. 20.
    Marchesi, J.R., Ravel, J.: The vocabulary of microbiome research: a proposal. Microbiome 3, 31 (2015)CrossRefGoogle Scholar
  21. 21.
    Janda, J.M., Abbott, S.L.: 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45, 2761–2764 (2007)CrossRefGoogle Scholar
  22. 22.
    Cock, P.J.A., Fields, C.J., Goto, N., Heuer, M.L., Rice, P.M.: The sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 38, 1767–1771 (2010)CrossRefGoogle Scholar
  23. 23.
    Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R.: QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010)CrossRefGoogle Scholar
  24. 24.
    DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., Andersen, G.L.: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)CrossRefGoogle Scholar
  25. 25.
    Yilmaz, P., Parfrey, L.W., Yarza, P., Gerken, J., Pruesse, E., Quast, C., Schweer, T., Peplies, J., Ludwig, W., Glockner, F.O.: The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014)CrossRefGoogle Scholar
  26. 26.
    McMurdie, P.J., Holmes, S.: phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013)CrossRefGoogle Scholar
  27. 27.
    Oksanen, J., Blanchet, G.F., Kindt, R., Legendre, P., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H., Wagner, H.: vegan: Community Ecology Package, R package version 2.3-0 (2015)Google Scholar
  28. 28.
    Love, M.I., Huber, W., Anders, S.: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)CrossRefGoogle Scholar
  29. 29.
    Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)CrossRefGoogle Scholar
  30. 30.
    Mandal, S., Van Treuren, W., White, R.A., Eggesbo, M., Knight, R., Peddada, S.D.: Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015)Google Scholar
  31. 31.
    Rivera-Pinto, J., Egozcue, J.J., Pawlowsky-Glahn, V., Paredes, R., Noguera-Julian, M., Calle, M.L.: Balances: a new perspective for microbiome analysis. mSystems 3(4), e00053-18 (2018)Google Scholar

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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