DOI: 10.5061/dryad.w3r2280xm
title: Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes

description: DNA metabarcoding is used to enumerate and identify taxa in both
 environmental samples and tissue mixtures, but the effectiveness of
 particular markers depends on their sensitivity to the taxa involved.
 Using multiple primer sets that amplify different genes can mitigate
 biases in amplification efficiency, sequence resolution, and reference
 data availability, but few empirical studies have evaluated markers for
 complementary performance. Here, we assess the individual and joint
 performance of 22 markers for detecting species in a DNA pool of 98
 species of marine and freshwater bony fishes from geographically and
 phylogenetically diverse origins. We find that a portfolio of four markers
 targeting 12S, 16S, and two regions of COI identifies 100% of reference
 taxa to family and nearly 60% to species. We then use these four markers
 to evaluate metabarcoding of heterogeneous tissue mixtures, using
 experimental fishmeal to test: 1) the tissue input threshold to ensure
 detection; 2) how read depth scales with tissue abundance; and 3) the
 effect of non-target material in the mixture on recovery of target taxa.
 We consistently detect taxa that make up &gt;1% of fishmeal mixtures
 and can detect taxa at the lowest input level of 0.01%, but rare taxa
 (&lt;1%) were detected inconsistently across markers and replicates.
 Read counts showed only a weak correlation with tissue input, suggesting
 they are not a reliable quantitative proxy for relative abundance. Despite
 the limitations arising from primer specificity and reference data
 availability, our results demonstrate that a modest portfolio of markers
 can perform well in detecting and identifying aquatic species in complex
 mixtures despite heterogeneity in tissue representation, phylogenetic
 affinities, and from a broad geographic range.
description: Metabarcoding markers Twenty-two markers for
 mitochondrial (COI, 12S, 16S) and nuclear (18S, 28S) barcoding genes were
 selected from metabarcoding, eDNA, and Sanger sequencing barcoding studies
 of marine and freshwater fishes, including seafood products (Table 1).
 Most of these markers were designed to target bony fishes (teleosts), but
 we added markers targeting elasmobranchs, crustaceans, and cephalopods –
 taxonomic groups that are often poorly resolved by universal fish
 barcodes. Only markers that amplified targets &lt;300 bp were selected
 because shorter fragments are more likely to amplify degraded DNA
 (Devloo-Delva et al., 2019, Shokralla et al., 2015; Staats et al., 2016),
 which is expected to be the case for highly-processed fishmeal and
 oil. Reference DNA pools To
 compare the amplification and resolution of the 22 markers before
 determining complementarity, we constructed two pools with equal
 concentrations of extracted DNA from 98 marine and freshwater teleost
 fishes and five elasmobranch, crustacean, and cephalopod species, in total
 spanning 88 genera and 60 families (full reference pool; Table 2). Samples
 were obtained primarily from vouchered collections, but also from fish
 markets to encompass commercially-important groups. We sampled muscle
 tissue from inside the body wall (i.e., no surface contact) for DNA
 extractions, in an attempt to avoid trace contamination from contact with
 other species. To further minimize the potential for detecting false
 positives from tissue contamination, we constructed a second, more
 restricted reference pool including only the 73 DNA extracts from
 vouchered museum specimens (vouchered reference pool).
 Experimental tissue mixture samples
 Metabarcoding is typically used to detect both rare and abundant
 constituents in mixtures, and most applications include species in unequal
 proportions along with varying amounts and types of non-target material.
 In aquaculture feeds, we will refer to the non-target material as
 “filler.” To evaluate detection power in actual tissue mixtures (as
 opposed to pools of DNA extracts), we used fishmeal mixed with different
 fillers. The purpose of the filler was to test whether metabarcoding data
 are negatively impacted by fillers, either because of a loss of on-target
 sequencing reads or because of potential PCR inhibition. Similarly complex
 and heterogeneous mixtures of tissue sources might be expected in gut
 content or fecal samples in more ecological applications.
 To create experimental fishmeal, we freeze-dried tissue from 30
 of the unvouchered fish species in the full reference pool (muscle tissue
 from market samples; whole fish from research samples), coarsely
 homogenized each sample in a coffee grinder, and then finely ground using
 a freezer mill where each tissue sample is pulverized within a container
 submerged in liquid nitrogen. Each species was added one-by-one, and we
 cleaned all containers and tools by wiping them with a 10% bleach solution
 followed by 70% ethanol to decontaminate between samples. Species were
 assigned to one of six abundance levels: 13.33%, 3.65%, 1.91%, 1%, 0.1%,
 or 0.01% of the mixture (by weight), thereby spanning &gt;3 orders of
 magnitude variation in representation (Table 3). Each abundance level was
 represented by five species, which were assigned to balance freshwater and
 marine habitats, major phylogenetic groups, and degree of fishery interest
 across levels. This experimental design allowed us to assess how dominant
 and rare taxa added at discrete proportions to a heterogeneous mixture
 relate to the proportion of sequencing reads attributed to each taxon and
 to compare amplification biases across multiple taxa added in the same
 amount to the fishmeal. To test the effect of the
 non-target material, the fishmeal mixtures were combined with two unique
 fillers for a total of seven individual experimental feeds with low (2%),
 medium (10%), and high (25%) proportions of fishmeal relative to filler
 (Table 3). Fillers included plant-derived materials – grain and grass
 flours – and animal byproducts – bloodmeal and feathermeal – to represent
 mixture constituents used in aquaculture feeds. Fishmeal proportions also
 mimicked potential levels of fish tissue added to aquaculture feeds, from
 low (0%-2%) to high (25%) proportions of fish in the feed mixture. By
 multiplying the proportion of fishmeal in the experimental feed by the
 proportion of a particular fish species in the fishmeal, we could test the
 detection threshold for individual taxa down to 0.0002% of total
 experimental mixture mass (i.e., minimum of 0.01% of a particular species
 in the fishmeal and 2% fishmeal in the feed). DNA
 extracts were quantified by a Qubit fluorometer (high-sensitivity or
 broad-range dsDNA assay depending on concentration range), diluted with
 DNAse-free water, and added in equal proportion to the full reference and
 vouchered reference DNA pools. DNA extracts from the 30 fishmeal species
 were combined in two additional mock DNA pools: one with equal
 concentration among all taxa (mock equal) and the other in which DNA
 extract concentration was proportionate to the amount of tissue included
 in the fishmeal (mock variable). Similar to the previous reference DNA
 pools, DNA pools for the mock equal and mock variable pools were prepared
 in triplicate (Fig. S1). Metabarcoding sequencing
 libraries were prepared from each pool using a two-step amplicon protocol
 (D’Aloia, Bogdanowicz, Harrison, & Buston, 2017) in which an
 initial PCR targets the gene region of interest using locus-specific
 primers with Nextera 5’ tails (5’-TC GTCGGCAGCGTCAGATGTGTATAAGAGACAG
 appended to each forward primer and 5’ -GTCTCGTGGGCTC
 GGAGATGTGTATAAGAGACAG to each reverse primer, details in the SI). Equal
 volumes of the locus-specific PCR products for each sample were pooled and
 a second PCR added Nextera-style sequencing adapters with unique i5 and i7
 indexes that allow sequencing reads to be assigned to samples during
 analysis (details about reagent concentrations and PCR conditions in the
 SI). Rather than using combinatorial indexing, which can lead to
 mis-assigned reads caused by index-swapping (Carøe & Bohmann,
 2020; Schnell, Bohmann, & Gilbert, 2015), we used
 custom-synthesized adapters with unique dual indexes (Table S1) that can
 unequivocally identify samples by 8-base indexes on both ends of the
 molecule. For each sample, PCR products for all markers
 were pooled into a single indexed library and sequenced using paired-end
 150-bp on one lane of a HiSeq X Ten (Novogene, Inc.) with 15% PhiX to
 account for moderately low library complexity (following Aizpurua et al.,
 2018).
description: # Data for: Optimizing a metabarcoding marker portfolio for species
 detection from complex mixtures of globally diverse fishes
 [https://doi.org/10.5061/dryad.w3r2280xm](https://doi.org/10.5061/dryad.w3r2280xm) This repository includes: 1. the NCBI SRA information where the sequencing data are deposited, 2. the bioinformatic scripts to process the sequencing data, and 3. a link to the Git repository that includes intermediate data files (metabarcoding feature and taxonomy tables) and the R markdown files for analyzing the data for the manuscript and generating figures for the publication. ## Data Sequencing fastq files are deposited in the NCBI SRA Bioproject # PRJNA1021650. All analyses begin with these fastq files and follow the analysis outlined in the scripts below. ## Analysis Scripts for initially processing the raw data files are included in this Dryad repository. * ncbi\_database\_download.sh - This script provides code for downloading the NCBI nucleotide databases to be used with local BLAST for generating taxonomy assignments. The NCBI databases must be downloaded prior to running the analysis scripts. * example\_settings\_file.txt - An example format for the Settings file required for the multilocus\_metabarcoding\_pipeline.sh script. * example\_loci\_file.txt - An example format for the Locus file required for the multilocus\_metabarcoding\_pipeline.sh script. * accession\_to\_lineage\_blast.sh - A script that uses the software \`taxonkit\` to link the BLAST accession number to taxonomic lineage. * multilocus\_metabarcoding\_pipeline.sh - The script that performs the metabarcoding data analysis for the loci in the Locus file based on the settings in the Settings file. Outputs from those scripts are the metabarcoding feature and taxonomy tables that are used as inputs for the R markdown files accessible on GitHub at the following repository: * https://github.com/DianaBaetscher-NOAA/marker-portfolio