Teaching‎ > ‎

Learning QIIME

There are already a number of tutorials on the QIIME website.  The following supplemental tutorials focus more on the individual steps to 16S rRNA gene sequencing data analysis, to help you understand the steps and how all the options are used in the command line. These tutorials are intended primarily for a student audience, but hopefully they may be helpful to others as well.

Installing QIIME

To do these tutorials, you will need QIIME installed on your computer. There are two easy options for installing QIIME (or, if you're a Linux geek and like compiling code, you could install from scratch).  I'm going to focus the following tutorials on using MacQIIME in Mac OS X, but you can also use the QIIME VirtualBox in Windows, Mac or Linux. Chose either of these options for installation:
  • MacQIIME. If you're in Mac OS X, I'd recommend installing MacQIIME. To do so, please follow these instructions. Additionally, you will also want to install BLAST if you don't already have it.
  • VirtualBox. Alternatively, you may need to use the QIIME VirtualBox if you are in Windows. Think of this VirtualBox (VB) as a separate computer running within your computer. It has its own filesystem, contained within the .vbi disk image, so if you want to get files off of or on to the VB, you have to transfer them.

Learning QIIME

QIIME Overview Tutorial - a modification of the Overview Tutorial on qiime.org. Goes through the steps of dereplicating barcodes/samples, denoising 454 reads, picking OTUs, assigning taxonomy, and analyzing alpha and beta diversity. The overall goal of this tutorial is for you to understand the logical progression of steps in a high-throughput amplicon sequencing data analysis pipeline. It should stand on its own as a self-guided process, but please feel free to ask me any questions you may have.

Exploratory Project: Bacterial Communities in Anaerobic Digesters. This tutorial is intended for a classroom setting where students (already trained via the overview tutorial above) can explore a new data set on their own. It does not have step-by-step instructions. Rather, it's open-ended, and poses more in-depth questions.  While the previous overview tutorial stands on its own, this second exploratory project relies on independent inquiry and problem-solving, and it might work best if guided by an instructor or more experienced colleague.