POAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data
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High-throughput DNA metabarcoding enables large-scale biodiversity assessment by identifying taxa from environmental samples, but its accuracy critically depends on denoising methods that separate true biological variation from PCR and sequencing errors. A persistent challenge is robust reconstruction of sequence diversity across abundance distributions, where low-abundance variants are particularly difficult to recover.
We introduce POAnoise , a graph-based denoising framework that uses Partial Order Alignment (POA) to model relationships among noisy sequencing reads. POAnoise incrementally constructs sequence graphs that represent substitutions and indels, and derives consensus sequences from graph-supported paths using a weighted consensus strategy. By combining graph-based alignment with abundance-aware clustering, the method provides a structured way to reconstruct sequence variants from noisy amplicon data across heterogeneous abundance regimes.
We evaluated POAnoise on simulated ITS and 16S datasets and compared its performance with established denoising methods, DADA2 and UNOISE3, across multiple parameter settings. Across the benchmark datasets, POAnoise generally achieved higher F 1 -scores and exhibited more stable performance across parameter configurations. In abundance-aware analyses, POAnoise showed reconstruction ratios closer to unity and reduced abundance-dependent deviation compared with DADA2, while remaining broadly comparable to UNOISE3 across most abundance classes. Overall, these results indicate that POAnoise can provide a robust alternative for amplicon denoising.
Author Summary
DNA sequencing allows us to study the diversity of microorganisms in environmental samples, but errors introduced during laboratory and sequencing processes can make it difficult to distinguish real biological sequences from noise. This is particularly challenging when some sequences are much more abundant than others. In this study, we developed POAnoise, a graph-based approach for reducing errors in amplicon sequencing data by using relationships among similar sequences to reconstruct the underlying biological diversity. We tested POAnoise on simulated bacterial and fungal sequencing data and compared it with commonly used denoising methods. We found that POAnoise generally recovered true sequences accurately and showed consistent performance across different settings and abundance levels. Our results suggest that considering relationships among sequences can improve the reliability of amplicon sequencing analyses and may help researchers obtain more consistent estimates of microbial diversity.
