Disparities in second‐generation DNA metabarcoding results exposed with accessible and repeatable workflows

Abstract

Different second-generation sequencing technologies may have taxon-specific biases when DNA metabarcoding prey in predator faeces. Our major objective was to examine differences in prey recovery from bat guano across two different sequencing workflows using the same faecal DNA extracts. We compared results between the Ion Torrent PGM and the Illumina MiSeq with similar library preparations and the same analysis pipeline. We focus on repeatability and provide an R Notebook in an effort towards transparency for future methodological improvements. Full documentation of each step enhances the accessibility of our analysis pipeline. We tagged DNA from insectivorous bat faecal samples, targeted the arthropod cytochrome c oxidase I minibarcode region and sequenced the product on both second-generation sequencing platforms. We developed an analysis pipeline with a high operational taxonomic unit (OTU) clustering threshold (i.e., ≥98.5%) followed by copy number filtering to avoid merging rare but genetically similar prey into the same OTUs. With this workflow, we detected 297 unique prey taxa, of which 74% were identified at the species level. Of these, 104 (35%) prey OTUs were detected by both platforms, 176 (59%) OTUs were detected by the Illumina MiSeq system only, and 17 (6%) OTUs were detected using the Ion Torrent system only. Costs were similar between platforms but the Illumina MiSeq recovered six times more reads and four additional insect orders than did Ion Torrent. The considerations we outline are particularly important for long-term ecological monitoring; a more standardized approach will facilitate comparisons between studies and allow faster recognition of changes within ecological communities.

Promises and pitfalls of using high‐throughput sequencing for diet analysis

“…At its heart, metabarcoding is a PCR-based targeted approach in which only taxonomically informative DNA markers (i.e., barcodes) are targeted for PCR amplification and sequencing (Taberlet, Coissac, Pompanon, Brochmann, & Willerslev, 2012). While previously based on cloning and Sanger sequencing of individual bacterial colonies (Alberdi, Garin, Aizpurua, & Aihartza, 2012), today PCR products are deepsequenced on high-throughput sequencing platforms such as the lllumina MiSeq or the IonTorrent PGM (Divoll, Brown, Kinne, McCracken, & O'Keefe, 2018). Thanks to unique short nucleotide sequences appended to the 5' end of metabarcoding primers (usually called "tags" or "MIDs"; Binladen et al (2007) and/or indices to separate sequencing libraries (Murray, Coghlan, & Bunce, 2015), or both , each resulting sequence can be linked to the corresponding PCR replicate and sample.…”

“…). For example, one recent study on bat diet reported that only 35% of OTUs were shared amongst results when performing workflows for metabarcoding and sequencing of the same DNA extracts on different sequencing platforms(Divoll et al, 2018). Others reported differences between repeated sequencing runs of microbiome libraries on the same sequencing platform(Chase et al, 2016).Hence, if multiple sequencing runs are required, it is advisable to split samples so that fractions of all samples are sequenced in all runs, or at least avoid sequencing all samples from contrasting groups in different sequencing runs to avoid artificially inflating differences between groups.…”

Gaps in DNA-Based Biomonitoring Across the Globe

“…In addition, detection of whole communities as opposed to fewer taxa will require a greater sequencing depth (Porter and Hajibabaei, 2018b). Similar to environmental sample type, the sequencing process of DNA-based biomonitoring is often referred to as "NGS, " "High-throughput sequencing (HTS), " and "Second-generation sequencing (2GS)" (Dickie et al, 2018;Divoll et al, 2018;Zinger et al, 2019); this varying use of terminology again adds another level of inconsistency to DNAbased biomonitoring. Referring to a consistent term for this sequencing technique, similar to the ontology discussed for sample terminology, would be beneficial.…”

Metabarcoding a diverse arthropod mock community

“…Illumina sequencers generate more reads (20 million–10 billion/run) with lower error rates than Ion Torrent platforms, but the latter instruments can deliver longer reads and can generate results more rapidly (Mardis et al, ). It is unclear how severely the choice of HTS platform affects species recovery as their performance has rarely been compared in eukaryotes (Divolli, Brown, Kinne, McCracken, & O’Keefe, ). However, work on microbial communities found general agreement between platforms although reads from Ion Torrent platforms were lower quality and more length variable than those from Illumina (Salipante et al, ; Tessler et al, ).…”