bioRxivMicrobiology
Relative performance of Oxford Nanopore MinION vs. Pacific Biosciences Sequel third-generation sequencing platforms in identification of agricultural and forest pathogens
K. Loit, K. Adamson, M. Bahram, R. Puusepp, S. Anslan, R. Kiiker, R. Drenkhan, L. Tedersoo
Abstract
Culture-based molecular characterization methods have revolutionized detection of pathogens, yet these methods are either slow or imprecise. The second-generation sequencing tools have much improved precision and sensitivity of detection, but the analysis processes are costly and take several days. Of third-generation techniques, the portable Oxford Nanopore MinION device has received much attention because of its small size and possibility of rapid analysis at reasonable cost. Here, we compare the relative performance of two third-generation sequencing instruments, MinION and Pacific Biosciences Sequel in identification and diagnostics of pathogens from conifer needles and potato leaves and tubers. We demonstrate that Sequel is efficient in metabarcoding of complex samples, whereas MinION is not suited for this purpose due to the high error rate and multiple biases. However, we find that MinION can be utilized for rapid and accurate identification of dominant pathogenic organisms from plant tissues following both amplicon-based and metagenomics-based approaches. Using the PCR-free approach with shortened extraction and incubation times, we performed the entire MinION workflow from sample preparation through DNA extraction, sequencing, bioinformatics and interpretation in two and half hours. We advocate the use of MinION for rapid diagnostics of pathogens, but care needs to be taken to control or account for all potential technical biases.\n\nIMPORTANCEWe develop new and rapid protocols for MinION-based third-generation diagnostics of plant pathogens that greatly improves the speed and precision of diagnostics. Due to high error rate and technical biases in MinION, PacBio Sequel platform is more useful for amplicon-based metabarcoding from complex biological samples.
- organism: plant (other)
- assay: long-read sequencing
- research focus: variant calling
- research focus: differential expression
- research focus: benchmarking
- research focus: database / resource
- approach: deep learning
- approach: statistical model
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