Visualizing Patterns of Marine Eukaryotic Diversity from Metabarcoding Data Using QIIME

  • Matthieu LerayEmail author
  • Nancy Knowlton
Part of the Methods in Molecular Biology book series (MIMB, volume 1452)


PCR amplification followed by deep sequencing of homologous gene regions is increasingly used to characterize the diversity and taxonomic composition of marine eukaryotic communities. This approach may generate millions of sequences for hundreds of samples simultaneously. Therefore, tools that researchers can use to visualize complex patterns of diversity for these massive datasets are essential. Efforts by microbiologists to understand the Earth and human microbiomes using high-throughput sequencing of the 16S rRNA gene has led to the development of several user-friendly, open-source software packages that can be similarly used to analyze eukaryotic datasets. Quantitative Insights Into Microbial Ecology (QIIME) offers some of the most helpful data visualization tools. Here, we describe functionalities to import OTU tables generated with any molecular marker (e.g., 18S, COI, ITS) and associated metadata into QIIME. We then present a range of analytical tools implemented within QIIME that can be used to obtain insights about patterns of alpha and beta diversity for marine eukaryotes.

Key words

Metabarcoding QIIME Alpha diversity Beta diversity Principal component analysis Rarefaction 



We thank Sarah Bourlat for inviting this submission. This work was supported by the Sant Chair and the Smithsonian Tennenbaum Marine Observatories Network, for which this is Contribution No. 3.


  1. 1.
    Mora C, Tittensor DP, Adl S et al (2011) How many species are there on Earth and in the Ocean? PLoS Biol 9:1001127. doi: 10.1371/journal.pbio.1001127 CrossRefGoogle Scholar
  2. 2.
    Appeltans W, Ahyong ST, Anderson G et al (2012) The magnitude of global marine species diversity. Curr Biol 22:2189–202. doi: 10.1016/j.cub.2012.09.036 CrossRefPubMedGoogle Scholar
  3. 3.
    Tittensor DP, Mora C, Jetz W et al (2010) Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098–101. doi: 10.1038/nature09329 CrossRefPubMedGoogle Scholar
  4. 4.
    Fonseca VG, Carvalho GR, Sung W et al (2011) Second-generation environmental sequencing unmasks marine metazoan biodiversity. Nat Commun 1:1–8. doi: 10.1038/ncomms1095 Google Scholar
  5. 5.
    Leray M, Knowlton N (2015) DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. Proc Natl Acad Sci U S A 112:2076–2081. doi: 10.1073/pnas.1424997112 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Leray M, Yang JY, Meyer CP et al (2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool 10:34. doi: 10.1186/1742-9994-10-34 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Taberlet P, Coissac E, Pompanon F et al (2012) Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 21:2045–50. doi: 10.1111/j.1365-294X.2012.05470.x CrossRefPubMedGoogle Scholar
  8. 8.
    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–6. doi: 10.1038/nmeth.f.303 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541. doi: 10.1128/aem.01541-09 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Angiuoli SV, Matalka M, Gussman A et al (2011) CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics 12:356. doi: 10.1186/1471-2105-12-356 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Hildebrand F, Tadeo R, Voigt A et al (2014) LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome 2:30. doi: 10.1186/2049-2618-2-30 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013) EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2:16. doi: 10.1186/2047-217X-2-16 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    McDonald D, Clemente JC, Kuczynski J et al (2012) The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience 1:7. doi: 10.1186/2047-217X-1-7 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Invertebrate ZoologyNational Museum of Natural History, Smithsonian InstitutionWashingtonUSA

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