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.

Featured Software from the Kartzinel Lab: Geographic Coverage of DNA Barcodes. The inaugural code repository to be highlighted in our Featured Software section of the Software & Data page presents the Quarto Code Book published in association with our Molecular Ecology Review Paper, "Global Availability of Plant DNA Barcodes as Genomic Resources to Support Basic and Policy-Relevant Biodiversity Research" can be easily modified to evaluate the geographic coverage of other data sets. Although the featured code emphasizes geographic coverage from our work in Yellowstone National Park...

Over the past couple of years, a lot of things have changed. Some of those changes have brought improvements to the way we share the software and data that we generate in the lab, necessitated in part by the growing popularity of our work to a point that we recognized a substantial benefit to enhancing our commitment to easy access and open-source principles. 

To make it easier to find and access our most in-demand repositories -- often spread across a variety of platforms including GitHub, Zenodo, NCBI, BOLD, etc. -- we have created a main landing page on the website where we have curated a mix of both recent and timeless contributions to the field. Check it out!

You might also want to check out our lab's main GitHub repository, where we are increasingly making our projects publicly available so you can benefit from any updates we've made since posting a version of record for the code with a prior publication (e.g., a static record with a Zenodo DOI). 

I will periodically post updates to this blog in order to highlight and summarize important updates to help ensure the community is aware, but we will no longer be using these pages to share data or code directly as we have in the past.

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.   

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!