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!
Following the recent publication of our plant DNA barcode library from Mpala Research Centre, Kenya, led by Brian Gill, we are happy to provide a set of files to serve as our local trnL-P6 reference library (version 2.0). These files were carefully prepared by Courtney Reed, to whom we are most grateful.
Computational resources kindly contributed and explained by members of our community.