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QIIME (canonically pronounced ‘Chime’) is a pipeline for performing microbial community analysis that integrates many third party tools which have become standard in the field. QIIME can run on a laptop, a supercomputer, and systems in between such as multicore desktops. Linux and Mac OS X are supported natively, and Windows, Linux, and Mac OS X are supported via the QIIME virtual box.
Rather than reimplementing commonly used algorithms, QIIME wraps popular implementations of those algorithms. This allows us to make use of the many excellent tools available in this area, and allows faster integration of new tools. If you use tools that you think would be useful additions to QIIME, consider submitting a feature request by creating a new issue on our GitHub issue tracker and labelling it as an enhancement. You will need to create a free GitHub account in order to create an issue.
A standard QIIME analysis begins with sequence data from one or more sequencing platforms, including Sanger, Roche/454, and Illumina GAIIx. QIIME can perform library de-multiplexing and quality filtering; denoising with AmpliconNoise or the QIIME Denoiser; OTU and representative set picking with uclust, cdhit, mothur, BLAST, or other tools; taxonomy assignment with uclust, BLAST, the RDP classifier, tax2tree, mothur, or rtax; sequence alignment with PyNAST, muscle, infernal, or other tools; phylogeny reconstruction with FastTree, raxml, clearcut, or other tools; alpha diversity and rarefaction, including visualization of results, using over 20 metrics including Phylogenetic Diversity, chao1, and observed species; beta diversity and rarefaction, including visualization of results, using over 25 metrics including weighted and unweighted UniFrac, Euclidean distance, and Bray-Curtis; summarization and visualization of taxonomic composition of samples using area, bar and pie charts along with distance histograms; and many other features. While QIIME is primarily used for analysis of amplicon data, many of the downstream analysis pipeline (such as alpha rarefaction and jackknifed beta diversity) can be performed on any type of sample x observation tables if they are formatted correctly.
QIIME includes parallelization capabilities for many of the computationally intensive steps. By default, these are configured to utilize a mutli-core environment, and are easily configured to run in a cluster environment. QIIME is built in Python using the open-source PyCogent toolkit. It makes extensive use of unit tests, and is highly modular to facilitate custom analyses.
We recommend that all QIIME users keep an eye on the QIIME blog for important announcements. You can subscribe to the RSS feed or sign up for e-mail notifications on the front page of the blog. This is a very low traffic list (typically around one message per month), and we will not share subscriber information with anyone.
There are several ways to get a working install of QIIME.
The easiest is to use our virtual machines:
- Virtual Box: The QIIME Virtual Box is an Ubuntu Linux virtual machine, pre-loaded with QIIME and its dependencies. This is the quickest way to start using QIIME. To get the Virtual Box, please see Virtual Box documentation.
- Amazon EC2 image: A QIIME EC2 image can be loaded in the Amazon Web Services cloud. For details, see EC2 documentation.
Alternatively, there are a few choices for automated installation of QIIME.
If you are instead interested in a native install of QIIME, you can choose between the QIIME base install, which provides access to the most commonly used features of QIIME (with default parameters), or the QIIME full install. For both, you should begin with the QIIME installation guide. For most users, the QIIME base install will be sufficient, at least when getting started.
If installing QIIME natively, you’ll need to choose between the release and development version of QIIME.
Stable Release: Currently the most stable version of QIIME is our 1.8.0 release, which you can download here. If you don’t know whether you want to install the release or development version of QIIME, you most likely want the release version.
Development Version: QIIME is under very active development. To get the latest development version of QIIME, you access our git repository, which is hosted on GitHub. While this code is subject to minor changes in interface, it will provide access to the latest and greatest features. The official web documentation is likely to be out-of-date with respect to the development software. You should instead refer to the documentation in Qiime/doc. Check out the latest version of QIIME using git with the command:git clone git://github.com/qiime/qiime.git Qiime
After installing QIIME, you should move on to the QIIME Illumina Overview Tutorial to analyze an example data set. As you begin using QIIME on your own data, you’ll want to refer to the QIIME tutorials and the QIIME documentation.
The only way to get help with QIIME is to search or ask questions in the QIIME Forum. This is a public space for users to ask questions, search previous questions and answers, and share any tips they’ve found. The QIIME Developers moderate this forum, and we aim to respond to questions within one working day.
Before requesting help with QIIME review this discussion to get information on what data to send to help us answer your question as quickly as possible.
Users can also submit bug reports and feature requests via our GitHub issue tracker.
QIIME is an open-source project, primarily developed in the Knight Lab at the University of Colorado at Boulder, the Caporaso Lab at Northern Arizona University and the Clemente Lab at Icahn School of Medicine at Mount Sinai. If you are interested in getting involved, check out the developer notes.
If you use QIIME for any published research, please include the following citation:
QIIME allows analysis of high-throughput community sequencing data
J Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh, Kyle Bittinger, Frederic D Bushman, Elizabeth K Costello, Noah Fierer, Antonio Gonzalez Pena, Julia K Goodrich, Jeffrey I Gordon, Gavin A Huttley, Scott T Kelley, Dan Knights, Jeremy E Koenig, Ruth E Ley, Catherine A Lozupone, Daniel McDonald, Brian D Muegge, Meg Pirrung, Jens Reeder, Joel R Sevinsky, Peter J Turnbaugh, William A Walters, Jeremy Widmann, Tanya Yatsunenko, Jesse Zaneveld and Rob Knight; Nature Methods, 2010; doi:10.1038/nmeth.f.303