Read-Level Error Characterization of Rolling-Circle Amplification-Based Nanopore Sequencing of the Circular DNA Virome

Oxford Nanopore technology enables cost-effective, portable, long-read analyses of pathogen genomes. Accurate detection and interpretation of small circular viral genomes, including Anelloviridae, remain challenging due to limited base-level error quantification in rolling-circle amplification (RCA)-derived datasets. Here, we characterized read-level sequencing error profiles using M13mp18, a 7.2 kb circular phage genome, subjected to 1X and 3X shearing during library preparation. M13mp18 DNA was serially diluted into pooled anellovirus-positive plasma DNA extracts. Using custom error-analysis pipelines, we quantified mismatch, insertion, and deletion rates and evaluated consensus reconstruction accuracy across simulated sequencing depths. Since metagenomic viromes contain mixtures of related genomes and uneven coverage across taxa, depth-normalized subsampling was used to assess the precision of read-level error estimates under heterogeneous coverage. Across four benchmarked datasets, per-base error rates ranged from 0.018 to 0.022 errors per aligned base. Complete M13mp18 reference reconstruction was achieved at input levels ≥ 4.6 log10 copies, and consensus sequences reached 100% identity at depths ≥ 15X when sufficient reads were available. Below 4.6 log10 input copies, recovery was inconsistent. These findings provide a controlled empirical characterization of read-level error behavior in RCA-derived nanopore sequencing and support the interpretation of circular DNA virome data generated in complex metagenomic backgrounds.

Authors

Institutions

Publication Details

Journal
Viruses
Published
2026-06-26
DOI
https://doi.org/10.3390/v18070704
Primary Topic
Bacteriophages and microbial interactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Read-Level Error Characterization of Rolling-Circle Amplification-Based Nanopore Sequencing of the Circular DNA Virome

Kakhangchung Panmei, Steven J. Clipman, Dylan Duchen, Abraham J. Kandathil et al.
Viruses
Bacteriophages and microbial interactions
article

Read-Level Error Characterization of Rolling-Circle Amplification-Based Nanopore Sequencing of the Circular DNA Virome

Kakhangchung Panmei, Steven J. Clipman, Dylan Duchen, Abraham J. Kandathil, Florencia Martino, David L. Thomas
article en

Abstract

Oxford Nanopore technology enables cost-effective, portable, long-read analyses of pathogen genomes. Accurate detection and interpretation of small circular viral genomes, including Anelloviridae, remain challenging due to limited base-level error quantification in rolling-circle amplification (RCA)-derived datasets. Here, we characterized read-level sequencing error profiles using M13mp18, a 7.2 kb circular phage genome, subjected to 1X and 3X shearing during library preparation. M13mp18 DNA was serially diluted into pooled anellovirus-positive plasma DNA extracts. Using custom error-analysis pipelines, we quantified mismatch, insertion, and deletion rates and evaluated consensus reconstruction accuracy across simulated sequencing depths. Since metagenomic viromes contain mixtures of related genomes and uneven coverage across taxa, depth-normalized subsampling was used to assess the precision of read-level error estimates under heterogeneous coverage. Across four benchmarked datasets, per-base error rates ranged from 0.018 to 0.022 errors per aligned base. Complete M13mp18 reference reconstruction was achieved at input levels ≥ 4.6 log10 copies, and consensus sequences reached 100% identity at depths ≥ 15X when sufficient reads were available. Below 4.6 log10 input copies, recovery was inconsistent. These findings provide a controlled empirical characterization of read-level error behavior in RCA-derived nanopore sequencing and support the interpretation of circular DNA virome data generated in complex metagenomic backgrounds.

VirusesVol. 18(7)
Johns Hopkins University (US), Johns Hopkins Medicine (US), Yale University (US)
National Institutes of Health
Openalex Percentile: Top 7%
Bacteriophages and microbial interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.