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Learn how to perform a complete microbiome analysis using raw DNA data to create publication-ready figures.
Thank you for your interest in attending the applied microbiome analysis 3-hour workshop. We will be using Quantitative Insights into Microbial Ecology (QIIME) 2 for our microbiome analysis, but please note this is not an official QIIME 2-sponsored workshop. Most official QIIME 2 workshops are several days, meaning the following suggested reading is especially important preparation to help you get the most out of a shorter workshop.
There is a lot of jargon, so please do not get discouraged if some terms are confusing. These concepts will be clarified further in the workshop.
You will get more out of the workshop if you install QIIME 2 software prior to the workshop as it can be difficult to install. Installation directions are here. You can search the forum or post for help if you have any issues. For this length workshop it is still possible to follow along using only a web browser; updated Chrome is recommended.
Brief introduction to microbiome analysis
QIIME 2 overview
We will be using a command-line user interface for our analysis which means you will want to familiarize yourself with basic command line.
QIIME 2 glossary and core concepts
Jennifer Fouquier is pursuing her PhD in Computational Bioscience at the University of Colorado Anschutz Medical Campus in Aurora, Colorado. She is funded as an NIH/NLM T15 Informatics trainee under the guidance of Dr. Catherine Lozupone. She began her career in the wet lab, received her BS in Microbiology from the University of California, San Diego (2007), and transitioned to bioinformatics. She received her MS in Bioinformatics and Medical Informatics from San Diego State University (2015) where she performed research on the fungal microbiome of the built environment. She then became a bioinformatics programmer at The Scripps Research Institute where she developed interactive web-based tools for a citizen science project on natural language processing. Jennifer’s current research involves the development of novel computational methods to identify important features in longitudinal microbiome studies using machine learning methods. Her projects include microbiome studies on autism spectrum disorder and a diet intervention in HIV-infected individuals.