By Hannah Hoff
We have been doing a lot of plant DNA barcoding to build a library for the plants of the Greater Yellowstone Ecosystem. In the process, we have done some refinement to our lab's DNA barcoding protocols to increase clarity, efficiency, and reproducibility. Updated links to these protocols are available on the lab's wiki under the "Plant Barcoding" section for: trnL, rbcL, matK, and trnH-psbA markers.
Collaborator Nick Harvey has kindly provided a formatted version of our current Mpala Plant DNA Barcode Reference Library that is suitable for taxonomic assignments using the R package dada2. You can download the fasta file of reference library v.2.0 (corresponding to Gill et al. 2019) formatted for dada2 here.
Thank you, Nick, for making this time-saving resource available to share!
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.
Computational resources kindly contributed and explained by members of our community.