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Peer-reviewedCC BY 4.0PLOS One

Sub-consensus haploid variant calling in long-read sequencing technology

  • Xavier Zair1
  • Andreas Wilm2
  • Miles C. Benton3
  • Cheng Yong Tham4
  • Lin Yang
  • Paola Florez de Sessions
  • October Michael Sessions
  • Eng Hui Chew
  • Swapnil Mishra
4 affiliations
  1. 1National University of Singapore, Saw Swee hock School of Public Health, Singapore, Singapore
  2. 2ImmunoScape, Pte Ltd, Singapore, Singapore
  3. 3Oxford Nanopore Technologies, Oxford, UK
  4. 4Department of Pharmacy, National University of Singapore, Singapore, Singapore
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Abstract

Background: Next-generation sequencing (NGS) is vital for tracking viral evolution during outbreaks. Oxford Nanopore Technologies (ONT) offers long-read, cost-effective sequencing, but accurate sub-consensus variant detection remains challenging. LoFreq, a widely used caller for sub-consensus variant calling in haploid genomes, was designed for short reads. This study assesses and benchmarks LoFreq variant identification accuracy on long-read data and proposes a calibration method to improve accuracy. Methods: We created truth sets using three SARS-CoV-2 spike gene plasmids (7179 bases, 100 SNVs) and whole Escherichia coli genomes. Libraries were sequenced using R9.4.1 and R10.4.1 flow cells. LoFreq’s recall was benchmarked across chemistries and library sizes. We also introduced a method to adjust Phred quality scores for enhanced accuracy. Results: LoFreq showed high sensitivity, detecting variants at frequencies as low as 0.1%, with best performance on R10.4.1. However, false discovery rates (FDR) were chemistry- and depth-dependent. Our Phred score calibration reduced false positives while maintaining recall, though it was less effective below 10% frequency and not suitable for structural variant detection. Conclusion: LoFreq can detect sub-consensus variants in ONT data, but high FDR limits direct use. Our calibration method improves accuracy, offering a simple Phred score adjustment approach that reduces false discovery rates in ONT data.

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Cite

BibTeXRIS
BibTeX
@article{zair2026subconsensus,
  title = {{Sub-consensus haploid variant calling in long-read sequencing technology}},
  author = {Xavier Zair and Andreas Wilm and Miles C. Benton and Cheng Yong Tham and Lin Yang and Paola Florez de Sessions and October Michael Sessions and Eng Hui Chew and Swapnil Mishra},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0356259},
  url = {https://doi.org/10.1371/journal.pone.0356259},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Sub-consensus haploid variant calling in long-read sequencing technology
AU  - Xavier Zair
AU  - Andreas Wilm
AU  - Miles C. Benton
AU  - Cheng Yong Tham
AU  - Lin Yang
AU  - Paola Florez de Sessions
AU  - October Michael Sessions
AU  - Eng Hui Chew
AU  - Swapnil Mishra
PY  - 2026
DA  - 2026/10/05
JO  - PLOS One
DO  - 10.1371/journal.pone.0356259
UR  - https://doi.org/10.1371/journal.pone.0356259
AB  - Background: Next-generation sequencing (NGS) is vital for tracking viral evolution during outbreaks. Oxford Nanopore Technologies (ONT) offers long-read, cost-effective sequencing, but accurate sub-consensus variant detection remains challenging. LoFreq, a widely used caller for sub-consensus variant calling in haploid genomes, was designed for short reads. This study assesses and benchmarks LoFreq variant identification accuracy on long-read data and proposes a calibration method to improve accuracy. Methods: We created truth sets using three SARS-CoV-2 spike gene plasmids (7179 bases, 100 SNVs) and whole Escherichia coli genomes. Libraries were sequenced using R9.4.1 and R10.4.1 flow cells. LoFreq’s recall was benchmarked across chemistries and library sizes. We also introduced a method to adjust Phred quality scores for enhanced accuracy. Results: LoFreq showed high sensitivity, detecting variants at frequencies as low as 0.1%, with best performance on R10.4.1. However, false discovery rates (FDR) were chemistry- and depth-dependent. Our Phred score calibration reduced false positives while maintaining recall, though it was less effective below 10% frequency and not suitable for structural variant detection. Conclusion: LoFreq can detect sub-consensus variants in ONT data, but high FDR limits direct use. Our calibration method improves accuracy, offering a simple Phred score adjustment approach that reduces false discovery rates in ONT data.
SN  - 1932-6203
ER  -