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Visualizing Patterns of Marine Eukaryotic Diversity from Metabarcoding Data Using QIIME.

Matthieu Leray1, Nancy Knowlton2

  • 1Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, 10th and Constitution Ave., NW, Washington, DC, 20560, USA. leray.upmc@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|July 28, 2016
PubMed
Summary
This summary is machine-generated.

Researchers can now analyze massive marine eukaryotic diversity datasets using Quantitative Insights Into Microbial Ecology (QIIME). This open-source software visualizes complex patterns from high-throughput sequencing data, aiding taxonomic composition studies.

Keywords:
Alpha diversityBeta diversityMetabarcodingPrincipal component analysisQIIMERarefaction

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Area of Science:

  • Marine biology
  • Bioinformatics
  • Eukaryotic genomics

Background:

  • High-throughput sequencing of homologous gene regions is crucial for characterizing marine eukaryotic communities.
  • Massive datasets generated require advanced visualization tools for diversity and taxonomic analysis.
  • Existing microbial ecology software can be adapted for eukaryotic datasets.

Purpose of the Study:

  • To demonstrate the utility of Quantitative Insights Into Microbial Ecology (QIIME) for analyzing marine eukaryotic diversity.
  • To provide a guide for importing and analyzing large-scale eukaryotic sequencing data in QIIME.
  • To highlight QIIME's capabilities for visualizing alpha and beta diversity patterns.

Main Methods:

  • Utilizing PCR amplification and deep sequencing of eukaryotic gene regions.
  • Importing Operational Taxonomic Unit (OTU) tables and associated metadata into QIIME.
  • Applying QIIME's analytical and visualization tools for diversity assessment.

Main Results:

  • QIIME successfully imports and processes diverse eukaryotic molecular marker data (18S, COI, ITS).
  • The software provides effective visualization of complex alpha and beta diversity patterns.
  • Facilitates in-depth analysis of marine eukaryotic community composition.

Conclusions:

  • QIIME is a valuable, user-friendly tool for analyzing large marine eukaryotic sequencing datasets.
  • The described functionalities enable comprehensive insights into marine biodiversity.
  • Open-source bioinformatics tools are essential for advancing marine microbial ecology research.