Do any of the q2 users apply q2 with undergraduates? If so, what challenges or success have you had in using q2 with undergraduates? What advice do you have to introduce the students to q2? I have an initial raw sequencing data set that my undergraduate students will be helping me analyze this spring. I was thinking about working through one or more of the tutorials provided with my students, but you all may have other helpful resources as I am new to applying q2 to my research. I was curious how you might be incorporating bioinformatics into your curriculum. I primarily teach environmental biology and zoology.
I heard a few ideas at the recent workshop on using the amazon service to work with tutorials.
I haven’t used QIIME 2 with undergraduates (yet), but I did introduce QIIME 1 to quite a few students. I’m hoping to develop a course where I’m working, but postdocs are always busy .
I think a Jupyter Hub on AWS might be a good solution, particularly with some of the new extension architecture that lets you lock cells. (With the caveat that I haven’t played with it all that extensively.)
I would also check out qiita, which is a database and more gui-based platform. It utilizies QIIME 2 as an analysis approach, paired with a limited set of clustering/denoising algorithm. The paper that came out recently describes the philosophy/architecture better than I can. However, one advantage of the database from a teaching perspective is the ability to easily download and combine publicly available studies along with their metadata. Much (if not all) of the EMP data (paper here) is avaliable via Qiita.
@jwdebelius’s mention of different datasets for teaching reminds me: mock communities could be useful for teaching because they are usually small, easy-to-use data sets and have “known” compositions. We have a public database of mock communities, and I recently put together a tutorial showing how to use these.
Hi there! I’ve been able to set up some Jupyter Notebooks for QIIME 2 analysis. I set up the analysis to run on CyVerse’s Atmosphere VM resources. Students have to learn UNIX, navigating the file system and jupyter notebooks. Otherwise they enjoy looking at all the visualization files. In small groups, I compare the two types of denoising/clustering methods and taxonomic assignment results. I could share what I have developed with you. I work with my own generated data and published datasets.
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