Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples

Abstract

Adaptive sampling is a method of software-controlled enrichment unique to nanopore sequencing platforms. To test its potential for enrichment of rarer species within metagenomic samples, we create a synthetic mock community and construct sequencing libraries with a range of mean read lengths. Enrichment is up to 13.87-fold for the least abundant species in the longest read length library; factoring in reduced yields from rejecting molecules the calculated efficiency raises this to 4.93-fold. Finally, we introduce a mathematical model of enrichment based on molecule length and relative abundance, whose predictions correlate strongly with mock and complex real-world microbial communities.

ReadBouncer: precise and scalable adaptive sampling for nanopore sequencing

“…We expect that ReadBouncer can also contribute to the field of pathogen detection in non-model organisms. Metagenomics sequencing of such samples easily consists of up to 99% host reads that can be depleted with adaptive sampling resulting in an in-silico enrichment of pathogenic reads as shown by other research groups ( Marquet et al , 2022 ; Martin et al , 2022 ). Here, our CPU-based approach also makes access to adaptive sampling much easier for researchers studying wild living animals in the field.…”

Rapid PCR-Based Nanopore Adaptive Sequencing Improves Sensitivity and Timeliness of Viral Clinical Detection and Genome Surveillance

“…The length of DNA fragments from the samples is an important factor affecting the enrichment effect of adaptive sequencing, and libraries with longer fragment lengths showed better enrichment performance ( Zhao et al, 2021 ; Martin et al, 2022 ). In this study, the RPNAS workflow, which generated a longer length of library fragments, also showed better enrichment performance than that of the LNAS workflow.…”

“…Extraction of high molecular weight DNA can help obtain longer DNA fragments as much as possible, to improve the enrichment performance of adaptive sequencing ( Mayjonade et al, 2016 ; Angthong et al, 2020 ; Penouilh-Suzette et al, 2020 ; Boughattas et al, 2021 ; Jones et al, 2021 ). Previous studies have found that the loss of active channels was faster during adaptive sequencing ( Payne et al, 2021 ), and read rejections lead to a reduction of the overall data throughput ( Martin et al, 2022 ; Sun et al, 2022 ), which may decrease the enrichment effect of adaptive sequencing. We also observed a decline in the overall data yield during adaptive sequencing.…”

“…Therefore, increasing the enrichment efficiency is a key factor for the application of adaptive sequencing, which requires further optimization. The existing adaptive sequencing methods are primarily based on the alignment of nucleotide sequences or raw electrical signals, which could achieve approximately a 5-fold maximum enrichment of target genome data ( Gan et al, 2021 ; Kipp et al, 2021 ; Kovaka et al, 2021 ; Payne et al, 2021 ; Wanner et al, 2021 ; Martin et al, 2022 ). A deep-learning model distinguishes human DNA from bacterial DNA with over 90% accuracy and is faster than alignment-based approaches, which could represent an alternative option for adaptive sequencing to increase enrichment efficiency ( Bao et al, 2021 ).…”

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

“…They have also shown that the real-time analysis offered by Nanopore sequencing enabled a reduced turnaround time for pathogen identification [ 120 ]. The latest add-on feature with the ONT sequencing platform is “adaptive sampling”, which allows for enriching or depleting sequenced DNA from selected species selectively in a software-controlled manner during sequencing [ 120 , 121 ]. This is useful for clinical samples such as body fluids and swabs where human DNA largely outweighs non-human DNA, and the depletion of host DNA consequently increases the pathogen detection sensitivity [ 122 , 123 ].…”