Expectations and Realities of Adaptive Sampling in Nanopore Sequencing

Real-time selective sequencing on nanopore platforms offers a programmable way to enrich target molecules and deplete background DNA during a run. This approach, widely known as adaptive sampling (AS), has been applied across host depletion, metagenomics, targeted loci, plasmid/AMR workflows, and RNA/transcriptomic protocols, but reported performance varies substantially across studies. This review synthesizes current algorithmic and empirical evidence with emphasis on sequencing-relevant outcomes, including absolute informative yield, target-coverage behavior, throughput effects, and run-to-run stability. Across use cases, relative enrichment is frequently observed, but gains in usable genomic output are strongly conditioned by fragment-length distributions, reference quality, decision-loop latency, and rejection-associated penalties in total yield and pore longevity. Evidence from targeted-panel and complex-locus studies further indicates that improved depth concentration can coexist with coverage non-uniformity and context-specific trade-offs relative to wet-lab enrichment. Overall, the literature supports AS as a valuable but condition-dependent strategy whose benefit is greatest when assay design, reference selection, and computational constraints are jointly optimized.