Improving metabarcoding taxonomic assignment: A case study of fishes in a large marine ecosystem

Abstract

DNA metabarcoding is an important tool for molecular ecology. However, its effectiveness hinges on the quality of reference sequence databases and classification parameters employed. Here we evaluate the performance of MiFish 12S taxonomic assignments using a case study of California Current Large Marine Ecosystem fishes to determine best practices for metabarcoding. Specifically, we use a taxonomy cross‐validation by identity framework to compare classification performance between a global database comprised of all available sequences and a curated database that only includes sequences of fishes from the California Current Large Marine Ecosystem. We demonstrate that the regional database provides higher assignment accuracy than the comprehensive global database. We also document a tradeoff between accuracy and misclassification across a range of taxonomic cutoff scores, highlighting the importance of parameter selection for taxonomic classification. Furthermore, we compared assignment accuracy with and without the inclusion of additionally generated reference sequences. To this end, we sequenced tissue from 597 species using the MiFish 12S primers, adding 252 species to GenBank's existing 550 California Current Large Marine Ecosystem fish sequences. We then compared species and reads identified from seawater environmental DNA samples using global databases with and without our generated references, and the regional database. The addition of new references allowed for the identification of 16 additional native taxa representing 17.0% of total reads from eDNA samples, including species with vast ecological and economic value. Together these results demonstrate the importance of comprehensive and curated reference databases for effective metabarcoding and the need for locus‐specific validation efforts.

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“…We processed sequences using the default parameters and assigned taxonomy using two CRUX- generated reference databases following the methods of Gold et al [ 18 ]. We first assigned taxonomy using the California Current Large Marine Ecosystem fish specific reference database [ 36 ]. Second, we used the CRUX -generated 12S reference database supplemented with California Current Large Marine Ecosystem fish specific references to assign taxonomy using all available 12S reference barcodes to identify any non-fish taxa following the methods of Gold et al [ 36 ] using a Bayesian cutoff score of 60.…”

“…We first assigned taxonomy using the California Current Large Marine Ecosystem fish specific reference database [ 36 ]. Second, we used the CRUX -generated 12S reference database supplemented with California Current Large Marine Ecosystem fish specific references to assign taxonomy using all available 12S reference barcodes to identify any non-fish taxa following the methods of Gold et al [ 36 ] using a Bayesian cutoff score of 60. Although CRUX relies on ecoPCR (version 1.0.1) [ 37 ], blastn (version 2.6.0) [ 38 ], and Entrez-qiime (version 2.0) [ 28 ] as dependencies, we note that Bayesian cutoff scores are not directly analogous to percent identity from blastn .…”

“…Although CRUX relies on ecoPCR (version 1.0.1) [ 37 ], blastn (version 2.6.0) [ 38 ], and Entrez-qiime (version 2.0) [ 28 ] as dependencies, we note that Bayesian cutoff scores are not directly analogous to percent identity from blastn . The BLCA classifier incorporates alignment metrics, including percent identity and percent overlap, into the underlying Bayesian model which then returns the Bayesian cutoff score metric as a measure of confidence for each taxonomic rank for a given ASV (See [ 36 ] and [ 28 ] for more detail).…”

“…The resulting Anacapa -generated taxonomic tables were transferred into R for further processing [ 36 , 39 ]. We then decontaminated the taxonomic tables using methods developed by Kelly et al [ 8 ] and McKnight et al [ 40 ] as implemented in Gold [ 41 ], which removes sequences from index hopping and negative controls and conducts a site occupancy model to identify true rare sequences ( S2 Appendix ).…”

“…not directly analogous to percent identity from blastn. The BLCA classifier incorporates alignment metrics, including percent identity and percent overlap, into the underlying Bayesian model which then returns the Bayesian cutoff score metric as a measure of confidence for each taxonomic rank for a given ASV (See [36] and [28] for more detail).…”