Biodiversity Soup II: A bulk-sample metabarcoding pipeline emphasizing error reduction

Abstract

<jats:title>Abstract</jats:title><jats:p><jats:list list-type="order"><jats:list-item><jats:p>Despite widespread recognition of its great promise to aid decision-making in environmental management, the applied use of metabarcoding requires improvements to reduce the multiple errors that arise during PCR amplification, sequencing, and library generation. We present a co-designed wet-lab and bioinformatic workflow for metabarcoding bulk samples that removes both false-positive (tag jumps, chimeras, erroneous sequences) and false-negative (‘drop-out’) errors. However, we find that it is not possible to recover relative-abundance information from amplicon data, due to persistent species-specific biases.</jats:p></jats:list-item><jats:list-item><jats:p>To present and validate our workflow, we created eight mock arthropod soups, all containing the same 248 arthropod morphospecies but differing in absolute and relative DNA concentrations, and we ran them under five different PCR conditions. Our pipeline includes qPCR-optimized PCR annealing temperature and cycle number, twin-tagging, multiple independent PCR replicates per sample, and negative and positive controls. In the bioinformatic portion, we introduce <jats:italic>Begum</jats:italic>, which is a new version of <jats:italic>DAMe</jats:italic> (Zepeda Mendoza <jats:italic>et al</jats:italic>. 2016. <jats:italic>BMC Res. Notes</jats:italic> 9:255) that ignores heterogeneity spacers, allows primer mismatches when demultiplexing samples, and is more efficient. Like <jats:italic>DAMe, Begum</jats:italic> removes tag-jumped reads and removes sequence errors by keeping only sequences that appear in more than one PCR at above a minimum copy number.</jats:p></jats:list-item><jats:list-item><jats:p>We report that OTU dropout frequency and taxonomic amplification bias are both reduced by using a PCR annealing temperature and cycle number on the low ends of the ranges currently used for the Leray-Fol-Degen-Rev primers. We also report that tag jumps and erroneous sequences can be nearly eliminated with <jats:italic>Begum</jats:italic> filtering, at the cost of only a small rise in drop-outs. We replicate published findings that uneven size distribution of input biomasses leads to greater drop-out frequency and that OTU size is a poor predictor of species input biomass. Finally, we find no evidence that different primer tags bias PCR amplification (‘tag bias’).</jats:p></jats:list-item><jats:list-item><jats:p>To aid learning, reproducibility, and the design and testing of alternative metabarcoding pipelines, we provide our Illumina and input-species sequence datasets, scripts, a spreadsheet for designing primer tags, and a tutorial.</jats:p></jats:list-item></jats:list></jats:p>

eDNA‐based biomonitoring at an experimental German vineyard to characterize how management regimes shape ecosystem diversity

“…All samples were trimmed, and adaptors and low‐quality reads were removed using AdapterRemoval/2.1.7 (Lindgreen, 2012). Filtered and trimmed reads were sorted according to primers and tags, using Begum (https://github.com/shyamsg/Begum), an updated version of the toolkit DAMe that was originally developed for the preprocessing of PCR replicated metabarcoding sequence datasets (Zepeda‐mendoza, Bohmann, Carmona Baez, Thomas, & Gilbert, 2016) and subsequently optimized for eliminating tag jumps and faulty sequences (Yang et al, 2020). Sequences were then filtered so that only sequences present in at least two out of three PCR replicates were kept (Alberdi et al., 2018).…”

Coming of age for COI metabarcoding of whole organism community DNA: towards bioinformatic harmonisation

“…Through the adaptation of the microbial metabarcoding method to wocDNA samples, specific protocols to sample, sort and enrich community samples for wocDNA metabarcoding have been developed, targeting different taxonomic fractions and types of samples (e.g., Andujar et al, 2018a;Arribas et al, 2016;Creedy et al, 2019;Elbrecht & Leese, 2017;Fonseca et al, 2010;Yu et al, 2012). Additionally, recent efforts to adapt and optimise existing methods are increasing efficiency and versatility, for example through nondestructive DNA extraction techniques that retain specimens for morphological vouchering (Marquina et al, 2019;Nielsen et al, 2019), or library preparation techniques tailored to metazoan samples (Yang et al, 2020). Although wocDNA COI metabarcoding remains in an expansive phase of development, standardisation in field and laboratory methods are emerging.…”

“…In contrast, there has been little advance in the development and validation of best practices associated with the bioinformatics processing of wocDNA COI metabarcoding data (but see Yang et al, 2020 for error reduction). Outside of taxonomic assignment, discussion of customising or parameterising tools for the purposes of working with wocDNA COI metabarcoding is very rare, with most papers simply reporting using tools with default settings.…”

“…Gilbert et al, 2010;. The majority of these adaptations have focused on sampling, and molecular laboratory steps, including adapted protocols to (i) sample, separate, enrich and/or clean animal wocDNA samples (Creedy et al, 2019;Fonseca et al, 2010Fonseca et al, , 2011, (ii) perform wocDNA extractions (Marquina et al, 2019;Nielsen et al, 2019), (iii) design and evaluate primers (Braukmann et al, 2019;Elbrecht & Leese, 2017, optimise amplification (Krehenwinkel et al, 2017) and prepare libraries (Yang et al, 2020). There is a growing consensus on the use of the mitochondrial cytochrome oxidase subunit I (COI) barcode, rather than other markers widely used for metabarcoding of non-metazoan communities, as the standard for wocDNA metabarcoding due to the range of COI primers with demonstrated efficiency (Braukmann et al, 2019;Elbrecht & Leese, 2017), and the potential of COI to improve the utility, resolution and reliability of wocDNA metabarcoding data (Andújar et al, 2018a;Turon et al, 2020).…”

Strategies for sample labelling and library preparation in DNA metabarcoding studies

“…However, it has to our knowledge not been formally tested whether -and to what extent -the shorter nucleotide tag additions in the tagged PCR approach offers greater PCR efficiency and taxonomic detection than the two other approaches, and thereby it can only be speculated that it is the most sensitive when it comes to detection of taxa in low abundance amongst the three main approaches. Regardless of metabarcoding strategy, we stress the importance of optimising PCR amplifications (usually by qPCR) to detect PCR inhibition, identify samples with low template quantity and track PCR efficiency issues (Murray et al2015;Yang et al 2021).…”