QIIME (pronounced "chime") stands for Quantitative Insights Into Microbial Ecology. QIIME is an open source software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data (such as SSU rRNA) generated on a variety of platforms, but also supporting analysis of other types of data (such as shotgun metagenomic data). QIIME takes users from their raw sequencing output through initial analyses such as OTU picking, taxonomic assignment, and construction of phylogenetic trees from representative sequences of OTUs, and through downstream statistical analysis, visualization, and production of publication-quality graphics. QIIME has been applied to single studies based on billions of sequences from thousands of samples.
QIIME is open source software. You can find the code under public revision control in our GitHub repository. You can find related projects under the QIIME organization on GitHub. We're very interested in community contributions and feedback. Use pull requests to contribute code or documentation, and our issue tracker to report bugs or request new features.
The quickest way to get started using QIIME is with the EC2 image or the VirtualBox. The QIIME overview tutorial is a good first analysis to run. In this tutorial you'll download a small data set and work through a series of commands that will introduce you to QIIME's most commonly used features and analyses.
Before requesting help with QIIME, please review this post.
For getting started on interacting with the command line, please review this post.
QIIME-deploy: Install QIIME easily on Ubuntu and RedHat Linux.
MacQIIME: Easy install of QIIME on MacOS X.
n3phele: Run QIIME on the Amazon Cloud from a web interface - no command line interaction is required.
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
You can find the QIIME paper here, and the data presented in this paper can be found
here.
Note that parts of QIIME (e.g., the OTU picking workflows) make use of other tools by wrapping them for convenient use with QIIME. For example, users using pick_otus_through_otu_table.py are actually using QIIME, uclust, RDP classifier, PyNAST, and FastTree, since QIIME is wrapping those applications. Any time you're using tools that QIIME wraps, it is appropriate to cite those tools as well.