Background Nanopore sequencing offers a cost-effective and portable platform for microbiome analysis, but amplicon-based approaches remain limited by higher sequencing error rates and a lack of workflows tailored to mixed domain ribosomal RNA profiling. While short-read technologies dominate microbial community analysis, their portability and flexibility are constrained. There is therefore a need for robust pipelines designed specifically for cross-domain Nanopore amplicon data. Results We introduce the Nanopore sequencing-based Amplicon Pipeline (NAP; <a href="https://github.com/Luke-B-Jones/NAP">https://github.com/Luke-B-Jones/NAP</a>), an open source workflow optimised for flexible mixed domain primer sets such as 515Y/926R. NAP combines dynamic quality filtering and base muting, chimera removal, centroid generation, BLAST-based taxonomic classification, hierarchical consensus correction, RAW-read reassignment, blank-informed decontamination, and domain-aware post-processing to produce curated genus level and species level abundance tables. Validation against logarithmic and gut commercial mock communities showed strongest performance at genus level, with reliable recovery above ca. 1% relative abundance and reproducible community reconstruction under Bray–Curtis, Jaccard, agreement plot, and Bland–Altman analyses. Internal benchmarking showed that dynamic filtering and base muting provided the most defensible balance between read quality, retained depth, and taxonomic fidelity across heterogeneous inputs, avoiding the sensitivity loss of fixed filtering approaches, and the reduced fidelity of overly permissive or aggressively masked alternatives. The consensus step substantially reduced raw centroid-based false positive burden in biological mocks by 82.9% at genus level and 78.8% at species level, while decontamination removed 7.00 ± 2.68 species level contaminant hits per replicate and adjusted a further 9.83 ± 6.49 abundances. Direct benchmarking against QIIME2 and Kraken2/Bracken showed that NAP best preserved expected community structure, with markedly fewer unexpected genera and stronger species level behaviour under the tested conditions. Synthetic ground truth benchmarking across richness/evenness panels, high similarity marker conflicts, and low abundance titrations further supported robustness: NAP produced no unsupported genus level calls, achieved genus level precision, recall, and F1-score of 1.000, 0.939, and 0.967 across community structure panels, and showed complete detection from ca. 1% relative abundance under default filtering. Residual species level errors were concentrated in high identity marker conflicts rather than arbitrary taxonomic assignments. Conclusions NAP provides a reproducible, flexible, domain-aware consensus workflow for cross-domain Nanopore amplicon profiling, with strongest support at genus level and competitive species level performance for well resolved taxa.

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data

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2026年6月29日

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Luke B. Jones,
Stefan Bagby

摘要

Background

Nanopore sequencing offers a cost-effective and portable platform for microbiome analysis, but amplicon-based approaches remain limited by higher sequencing error rates and a lack of workflows tailored to mixed domain ribosomal RNA profiling. While short-read technologies dominate microbial community analysis, their portability and flexibility are constrained. There is therefore a need for robust pipelines designed specifically for cross-domain Nanopore amplicon data.

Results

We introduce the Nanopore sequencing-based Amplicon Pipeline (NAP; https://github.com/Luke-B-Jones/NAP), an open source workflow optimised for flexible mixed domain primer sets such as 515Y/926R. NAP combines dynamic quality filtering and base muting, chimera removal, centroid generation, BLAST-based taxonomic classification, hierarchical consensus correction, RAW-read reassignment, blank-informed decontamination, and domain-aware post-processing to produce curated genus level and species level abundance tables. Validation against logarithmic and gut commercial mock communities showed strongest performance at genus level, with reliable recovery above ca. 1% relative abundance and reproducible community reconstruction under Bray–Curtis, Jaccard, agreement plot, and Bland–Altman analyses. Internal benchmarking showed that dynamic filtering and base muting provided the most defensible balance between read quality, retained depth, and taxonomic fidelity across heterogeneous inputs, avoiding the sensitivity loss of fixed filtering approaches, and the reduced fidelity of overly permissive or aggressively masked alternatives. The consensus step substantially reduced raw centroid-based false positive burden in biological mocks by 82.9% at genus level and 78.8% at species level, while decontamination removed 7.00 ± 2.68 species level contaminant hits per replicate and adjusted a further 9.83 ± 6.49 abundances. Direct benchmarking against QIIME2 and Kraken2/Bracken showed that NAP best preserved expected community structure, with markedly fewer unexpected genera and stronger species level behaviour under the tested conditions. Synthetic ground truth benchmarking across richness/evenness panels, high similarity marker conflicts, and low abundance titrations further supported robustness: NAP produced no unsupported genus level calls, achieved genus level precision, recall, and F1-score of 1.000, 0.939, and 0.967 across community structure panels, and showed complete detection from ca. 1% relative abundance under default filtering. Residual species level errors were concentrated in high identity marker conflicts rather than arbitrary taxonomic assignments.

Conclusions

NAP provides a reproducible, flexible, domain-aware consensus workflow for cross-domain Nanopore amplicon profiling, with strongest support at genus level and competitive species level performance for well resolved taxa.