Working with dietary DNA metabarcoding data? Unsure how to concisely summarize your workflow for publication? Tired of all the effort required to format your data tables for archiving in Dryad, supplementary materials, or other archives? The lab has posted new code to our GitHub repository that will help you solve all of these problems.

New feature on our Software & Data repository page: Hot off the press! Featuring code from Hannah Hoff's 2025 PNAS paper, The Apportionment of Dietary Diversity in Wildlife.

This paper presented a potentially paradigm-shifting strategy to quantify and characterize the number of unique 'diet types' that exist within a population or community. The strategy is based on a simple machine-learning algorithm and described in the Hoff et al. 2025 PNAS paper, which used the community of migratory large mammalian herbivores -- such as bison and elk -- as a prime example.

Fans of the lab will be very excited to see this much-anticipated release of our standard dietary DNA metabarcoding pipeline, with a walk-through easily accessible on the centralized "Software & Data" section of our webpage. Until now, people would have to access code repositories associated with each of our publications or contact us directly to model their analysis after our well-established workflow. That led to multiple versions of the pipeline in circulation, since we are constantly improving it and published versions quickly ended up out of date. We have tried to solve that problem by...

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.

As part of Beth's critical review in Molecular Ecology on abundance-filtering strategies in DNA metabarcoding pipelines, we conducted simulations and sensitivity analyses to illustrate how key assumptions in the design of our bioinformatic strategies can introduce biases that undermine ecological interpretations of the data. 

The Dryad repository for the paper contains data and code that will be useful for anyone who would like to replicate or enhance the simulations and/or sensitivity analyses. I consider this a major bioinformatic resource for researchers in the field, and an illustration of thoughtful research strategies that I hope others will build upon in a few key ways.

The simulations we conducted are relatively simple, but extremely relevant. It would be rewarding to explore the relevance of other assumptions, parameters, data structures, and/or downstream ecological metrics. This would not only be of fundamental interest, but the developments and insights would be profoundly useful for all researchers in the field (us included). The Reviewers and Editors of this original manuscript seemed to agree with that sentiment. We briefly considered publishing an R Shiny App or similar to facilitate this type of exploration -- I still think it could be worthwhile, so please let us know if you would like to contribute!

The sensitivity analyses model a strategy that I developed piecemeal over the years to help me check my assumptions about how robust my published conclusions would be and to be more persuasive with reviewers. Similar sensitivity analyses have been described in the supplementary materials in several publications in recent years. It requires a bit more work than simply using a plug-and-chug approach to bioinformatics and downstream analyses, but I think it pays off in terms of my own understanding of each study system and the reliability of my papers. I often encourage authors of papers that I review to consider doing something similar when their results are borderline, and I hope this code can serve as a resource to support that type of effort when appropriate.   

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.