POAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data
- 1Jyväskylän yliopisto: Jyvaskylan Yliopisto
Abstract
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 F1-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.
Related
- HiFiTaxa: a reproducible analysis pipeline for full length 16S rRNA gene and ITS amplicon sequencing from PacBio reads
- TACTIC: hybrid denoising and clustering for robust 16S amplicon diversity estimates
- Benchmarking 16S rRNA gene amplicon analysis in high-diversity microbial communities reveals fundamental trade-offs in clustering and denoising
- SeCAT (Sequence Consensus Amplicon Trimming): a simulation-informed Nextflow pipeline for cross-study harmonisation of 16S rRNA metabarcoding datasets
- ASPIRE: the Amplicon Sequencing Profiler for Investigating Respiratory Ecosystems
BibTeX
@misc{ardiyansyah2026poanoise,
title = {{POAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data}},
author = {M. Ardiyansyah and B. Furneaux and O. Ovaskainen},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.14.751445},
url = {https://www.biorxiv.org/content/10.64898/2026.09.14.751445v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - POAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data
AU - M. Ardiyansyah
AU - B. Furneaux
AU - O. Ovaskainen
PY - 2026
DA - 2026/09/14
PB - bioRxiv
DO - 10.64898/2026.09.14.751445
UR - https://www.biorxiv.org/content/10.64898/2026.09.14.751445v1
AB - 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 F1-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.
ER -