NanoPrism: A Taxonomy-Guided Pipeline for Rapid Functional Profiling of Oxford Nanopore Long-Read Metagenomes

Background/Objectives: Oxford Nanopore sequencing produces long reads quickly, but most functional profiling tools were developed for short reads or rely on assembly pipelines that are computationally costly and sensitive to long-read error rates. We present NanoPrism, a taxonomy-guided pipeline for rapid functional profiling of long-read metagenomes. Methods: NanoPrism (i) identifies sample composition with Kraken2, (ii) constructs compact species-specific coding sequence (CDS)–KEGG ortholog databases, and (iii) estimates ortholog abundances by direct minimap2 alignment of nanopore reads with single-copy marker normalization. We evaluated NanoPrism on simulated Pseudomonas aeruginosa PAO1 and PA14 reads and on ZymoBIOMICS mock-community datasets sequenced on GridION and PromethION platforms. Results: On the Zymo long-read datasets, NanoPrism achieved Pearson correlations of 0.917–0.922 against independent expected ortholog profiles under unit-sum normalization. On matched one-million-read subsets, NanoPrism achieved higher correlations and lower Jensen–Shannon distances and mean absolute errors than the evaluated DIAMOND-based MEGAN-LR workflow. Experiments that omitted one species at a time from the reference database showed that omission of low-abundance community members had limited effects on the aggregate KO profile, whereas omission of the dominant Listeria monocytogenes reference from the Log community reduced Pearson correlation from approximately 0.92 to 0.29. Conclusions: NanoPrism offers a computationally efficient option for taxonomy-guided functional profiling of bacterial isolates and defined microbial communities. Validation on complex clinical and environmental metagenomes, broader forms of taxonomic-classification error, and dedicated fungal benchmarks remain necessary.