QIIME2 Tutorial


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Overview of QIIME 2

QIIME 2 (Quantitative Insights Into Microbial Ecology 2) is an open-source bioinformatics platform that provides a suite of tools for analyzing and interpreting high-throughput sequencing data, particularly from microbiome studies. It is the successor to QIIME 1 and has been designed to handle a wide range of microbial community analyses, including but not limited to taxonomic, phylogenetic, compositional, and functional analyses.

The platform is highly versatile and supports various types of "features," which can be operational taxonomic units (OTUs), sequence variants, or other observational units like metagenome features. QIIME 2 is not limited to 16S rRNA gene sequencing data; it can also process other marker genes and whole-genome shotgun (WGS) data.

One of the core concepts in QIIME 2 is the use of the term "feature" instead of "OTU," which was commonly used in QIIME 1. This change reflects the broader scope of the platform, as it can handle different types of data and observational units. Features in QIIME 2 can be OTUs, sequence variants obtained through methods like DADA2 or Deblur, or metagenome features identified using tools like MetaPhlAn2.

QIIME 2 is designed to be user-friendly, with an emphasis on reproducibility and transparency in microbial community analysis. It provides a range of plugins and a flexible framework that allows users to develop and share new methods. The platform also includes extensive documentation, tutorials, and support through its community forum.


To install QIIME 2, users typically follow a few steps that involve creating a conda environment and installing the QIIME 2 package within that environment. The specific versions of QIIME 2 available for installation can vary, and users should choose the version that best suits their needs. The installation process is well-documented on the QIIME 2 website, and users can find detailed instructions for different operating systems.

Quick Start

For new users, QIIME 2 provides a "Moving Pictures" tutorial that serves as a quick start guide. This tutorial walks users through a sample analysis pipeline using a small dataset. It covers the basics of importing data, performing quality control, generating feature tables and representative sequences, taxonomic analysis, and creating visualizations. The tutorial is an excellent way for beginners to get hands-on experience with the platform and understand the workflow of a typical QIIME 2 analysis.

Code Examples Of Popular Commands

Here are five popular commands in QIIME 2, along with brief explanations:

  1. qiime tools import - This command is used to import data into QIIME 2. It supports various input formats and allows users to convert their data into a format that QIIME 2 can process.

  2. qiime demux summarize - After importing sequence data, this command helps visualize the quality of the sequences, which is crucial for making informed decisions about quality control steps.

  3. qiime dada2 denoise-single - For single-end sequence data, this command applies the DADA2 algorithm to model and correct sequencing errors, resulting in high-resolution sequence variants.

  4. qiime feature-classifier classify-sklearn - This command uses a pre-trained classifier to assign taxonomy to sequences. It is an essential step in understanding the composition of microbial communities.

  5. qiime diversity core-metrics-phylogenetic - This command calculates a suite of diversity metrics and generates visualizations that help interpret the diversity and structure of microbial communities.

These commands represent just a fraction of the functionality available in QIIME 2. The platform is continually evolving, with new plugins and updates that expand its capabilities and improve the user experience.

In conclusion, QIIME 2 is a powerful and flexible tool for microbial community analysis. Its comprehensive approach to handling different types of sequencing data, along with its commitment to reproducibility and transparency, makes it a valuable resource for researchers in the field of bioinformatics and beyond.