By Bethan Littleford-Colquhoun

A huge debt of gratitude to Beth for organizing a google drive folder that shares our strategy for initial processing of dietary DNA metabarcoding data. This directory provides template documents and code that we use to download paired-end read data from our dietary metabarcoding workflows, assemble forward and reverse reads, perform initial quality controls, and evaluate sequencing success. There is also a tutorial for how to upload sample data to SRA for archiving, which we have started to do routinely. Some details are specific to our dietary analysis sequencing workflow and/or the specific sequencing service that we use, plus we have particular sample naming schemes that we use, but it should be pretty straightforward to adapt for a variety of similar goals.

We are often asked to provide advice or assistance building plant DNA reference libraries for use in dietary metabarcoding projects. To begin centralizing info on our methods and sharing some important lessons-learned from experience, I have created a section on the lab's wiki for building plant barcode libraries. I will treat the google docs that you can link to from there as living documents. All of the details provided are nested within two main goals. The first goal is to collect plant voucher specimens and plant DNA barcode samples that match in ways that can be clearly documented  through their respective metadata sheets. This is critical for the long-term value of the data. The second goal is to ensure work done by field biologists and molecular biologists are mutually informative -- the best reference libraries are developed through the meaningful engagement of expert botanists who are knowledgeable in a local flora and the researchers who will be analyzing the laboratory data. 

We love to archive relevant vouchers in the Brown University Herbarium. Please keep in mind that the herbarium is staffed by expert botanists. Properly collected specimens can be mounted, archived, and digitized by professional staff -- this greatly reduces the cost and complexity of fieldwork. 

Prof Rebecca Kartzinel shared a tutorial on Brown University's high-performance computing cluster, called Oscar.

• Notes and code are available.