Speeding up the detection of invasive aquatic species using environmental DNA and nanopore sequencing

Abstract

22Traditional detection of aquatic invasive species, via morphological identification is often time-23 consuming and can require a high level of taxonomic expertise, leading to delayed mitigation 24 responses. Environmental DNA (eDNA) detection approaches of multiple species using Illumina-25 based sequencing technology have been used to overcome these hindrances, but sample 26 processing is often lengthy. More recently, portable nanopore sequencing technology has 27 become available, which has the potential to make molecular detection of invasive species more 28 widely accessible and to substantially decrease sample turnaround times. However, nanopore-29 sequenced reads have a much higher error rate than those produced by Illumina platforms, 30 which has so far hindered the adoption of this technology. We provide a detailed laboratory 31 protocol and bioinformatic tools to increase the reliability of nanopore sequencing to detect 32 invasive species, and we test its application using invasive bivalves. We sampled water from 33 sites with pre-existing bivalve occurrence and abundance data, and contrasting bivalve 34 communities, in Italy and Portugal. We extracted, amplified and sequenced eDNA with a 35 turnaround of 3.5 days. The majority of processed reads were ≥ 99 % identical to reference 36 sequences. There were no taxa detected other than those known to occur. The lack of 37 detections of some species at some sites could be explained by their known low abundances. 38

Takeaways from Mobile DNA Barcoding with BentoLab and MinION

“…The observed increase in samples having zero ambiguous bases points to an improved homopolymer resolution with the R10.3 chemistry. This marked improvement in R10.3 sequencing chemistry is a welcome development and paves the way for furthering nanopore sequencing applications such as DNA metabarcoding [ 82 , 83 , 84 , 85 ]. Error-prone reads from the R9 chemistry make it challenging to assign taxonomy [ 53 ], and previous studies have resorted to complex laboratory procedures [ 84 ] or reference-based polishing [ 82 ] to negate these sequencing errors.…”

“…This marked improvement in R10.3 sequencing chemistry is a welcome development and paves the way for furthering nanopore sequencing applications such as DNA metabarcoding [ 82 , 83 , 84 , 85 ]. Error-prone reads from the R9 chemistry make it challenging to assign taxonomy [ 53 ], and previous studies have resorted to complex laboratory procedures [ 84 ] or reference-based polishing [ 82 ] to negate these sequencing errors. One study tested the R10.3 chemistry for nanopore metabarcoding, but the only comparisons made to R9.4.1 chemistry were in terms of read coverage and read size distribution; no assessments were made on sequencing accuracy [ 83 ].…”

Anglers as Potential Vectors of Aquatic Invasive Species: Linking Inland Water Bodies in the Great Lakes Region of the US

“…Accordingly, there is no apparent single method of detection, prediction, or control of transmission. There have been numerous and varied approaches to detection, including basic manual sampling and monitoring [8] and inspections [9]; stakeholder and participant surveys to gauge presence and magnitude of impact [4,10] or develop conceptual models [11]; and analyzing eDNA [12]. Different approaches focus on modeling and predicting spread using risk assessment and screening tools [13,14], predicting egg transport [15], modeling propagule pressure [6,[16][17][18], and modeling of impedance surfaces to identify leastcost pathways of likely transmission [19].…”

The long and the short of it: Nanopore based eDNA metabarcoding of marine vertebrates works; sensitivity and specificity depend on amplicon lengths

“…The sample preparation and metabarcoding technique and workflow will determine the quality of the results and thus the species detection quality and possible biases (Beng & Corlett, 2020; van der Loos & Nijland, 2020). Important steps in the protocol include decisions about methods of sampling and DNA extraction (Bessey et al, 2020; Hunter, Ferrante, Meigs-Friend, & Ulmer, 2019), primer and PCR settings (Doi et al, 2019; Sard et al, 2019; Zhang, Zhao, & Yao, 2020), sequencing technology (Egeter et al, 2020; Singer, Fahner, Barnes, McCarthy, & Hajibabaei, 2019; Truelove, Andruszkiewicz, & Block, 2019), post sequencing data handling (Santos, van Aerle, Barrientos, & Martinez-Urtaza, 2020) and reference databases used (Hestetun et al, 2020; McGee, Robinson, & Hajibabaei, 2019).…”

“…Historically, the main limitation of nanopore sequencing was the relatively large error rate of 5 to 10% (Jain et al, 2015). This error rate can be overcome with bioinformatics tools to generate reliable consensus sequences and thus increase sequence accuracy (Baloğlu et al, 2020; Carradec et al, 2020; Egeter et al, 2020). To our knowledge, a bioinformatics pipeline is not yet available to generate such consensus sequences from raw sequence data in multiplexed metabarcoding experiments.…”