In my last post, I walked through the process of analyzing an amplicon sequence dataset with the DADA2 pipeline. At the end of that walkthrough, I combined an OTU table, taxonomy table, and sample metadata together into a Phyloseq object. This post will go through some of the basic data exploration we do in the Buckley lab with microbiome datasets. This is a total jumping off point, and the packages we use have much more depth than I explore here. This is just a walkthrough to show basic functionality instead of a highly specialized analysis pipeline.

In the Buckley lab, we have been using a MacGuyvered sequence analysis pipeline with components from Mothur, QIIME, and custom scripts for the majority of our amplicon sequence datasets. Before leaving for a new position, Chuck Pepe-Ranney (a previous postdoc in the Buckley lab) introduced me to the DADA2 pipeline and laid the groundwork for our lab’s transition to use it. I have since been working with fellow labmates to integrate DADA2 into our analytical pipeline and wanted to share how we’re using it today.

Job prospects for many graduate students and post docs seem daunting in the current academic market. We are constantly told how there are fewer and fewer tenure track positions while the number of PhDs continually rises. Much of our training is focused on the presumption that we will stay in the academy in a primary research or teaching position, leaving us to seek out non-academic job search skills on our own.