DOI: 10.5061/dryad.tdz08kpzx
title: Data from: The role of taxonomic expertise in interpretation of metabarcoding studies

description: The performance of DNA metabarcoding approaches for characterizing
 biodiversity can be influenced by multiple factors. Here we used
 morphological assessment of taxa in zooplankton samples to develop a large
 barcode database and to assess the congruence of taxonomic identification
 with metabarcoding under different conditions. We analyzed taxonomic
 assignment of metabarcoded samples using two genetic markers (COI, 18S
 V1-2), two types of clustering into molecular operational taxonomic units
 (OTUs, ZOTUs), and three methods for taxonomic assignment (RDP Classifier,
 BLASTn to GenBank, BLASTn to a local barcode database). The local database
 includes 1042 COI and 1108 18S (SSU) barcode sequences, and we added new
 high-quality sequences to GenBank for both markers, including 109
 contributions at the species level. The number of phyla detected and the
 number of taxa identified to phylum varied between genetic marker and
 among the three methods used for taxonomic assignments. Blasting the
 metabarcodes to the local database generated multiple unique contributions
 to identify OTUs and ZOTUs. We argue that a multi-marker approach combined
 with taxonomic expertise to develop a curated, vouchered, local barcode
 database increases taxon detection with metabarcoding, and its potential
 as a tool for zooplankton biodiversity surveys.
description: This data package includes
 data and code associated to the publication “The role of taxonomic
 expertise in interpretation of metabarcoding studies”. The data package
 has been organized as an R project, so if the R user downloads the full
 package including the file “StreamCode-Rproject.Rproj”, the scripts
 provided can be used without further path modifications. The project is
 organized as: Data folders (original-data,
 clean-data, input-data) Results folder
 R Code including two R files with functions and objects that are
 needed, and four rmarkdown files with code and text describing the
 analysis in the paper. Bibliography files
 Pdfs version of the
 rmarkdown files to give alternatives to non R users 
 The “original-data” folder
 includes the StreamCode_data.csv file with all the information for the
 zooplankton samples collected during the StreamCode project. The
 StreamCode_metadata.csv file describes the columns in the
 StreamCode_data.csv file. The “input-data” folder includes
 the results of the taxonomic assignment for the metabarcoding data, BLASTn
 results, taxonomy dictionaries used to standardize taxonomy between
 disparate datasets, information on sample location, and availability of
 images for the StreamCode samples. The “clean-data” folder has the
 final taxonomic assignment for the metabarcodes (OTUs and ZOTUs) for each
 genetic marker and for each method of taxonomic assignment. This is the
 final assignment after the confidence thresholds were applied and with the
 names matched to the WoRMS taxonomy (for marine organisms) or the NCBI
 taxonomy. The data in this folder is automatically generated when running
 the code in the file 01_CleanDatasets.Rmd. For the RDP Classifier the
 confidence threshold was set at 0.9, for the BLASTn the confidence
 thresholds were 85% percent coverage and 85% percent similarity.
 The “results” folder
 includes several tables with results, some that correspond with tables in
 the paper, some that were used as part of the analysis. These files were
 generated running the code in the rmarkdown files (01_CleanDatasets.Rmd,
 02_Barcodes_success_contributions.Rmd, 03_Barcodes_MethodsValidation.Rmd,
 04_MetabarcodingResults.Rmd). The genetic data associated with
 this project has been deposited in GenBank and can be found NCBI
 BioProject PRJNA421480
 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA421480). Specific
 information on accession numbers for each sample can be found in the
 publication Supplementary Material. All the details on data
 collection and data processing are available in the publication and the
 supporting information. The Supporting Information associated with the
 publication is included in the data package as an html file (that allows
 for interactive tables).
description: The R code used in the
 analysis is available in the R markdown files, which contains explanations
 of the different steps. We also provide the code and explanations as pdf
 files for non-R users. Names of files and folders aim to be as descriptive
 as possible. Regarding files and subfolders in
 the “input-data” folder: The names of the columns of the
 BLASTn results are ("seq.id", "match.accnum",
 "match.name", "evalue", "bitscore",
 "seq.length", "percent.match",
 "percent.coverage"), and are appended to the raw results files
 using the R code. The results from Midori are the
 original text files provided when running the query set of metabarcodes in
 the Midori server (http://www.reference-midori.info/server.php) on
 03-October-2020 using the Midori-LONGEST database and extracting the data
 in the “allrank” format. The raw ouput from Midori includes all the range
 of RDP Classifier probabilities, despite the default on the online server
 is fixed at 0.8. We used custom R functions (provided in the R file
 “00_FunctionsWeNeed.R”) to clean the Midori output and to filter out
 entries with confidence threshold lower than 0.9.