Investigating the performance of Oxford Nanopore long-read sequencing with respect to Illumina microarrays and short-read sequencing

Abstract

Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) has emerged as a promising tool for genomic analysis, but comprehensive comparisons with established platforms across diverse datasets remain limited. In this study, we present a comprehensive comparison of ONT long-read sequencing (LRS) against Illumina short- read sequencing (SRS) and microarray platforms across 14 human genomes.We performed ONT sequencing using both multiplexed and singleplexed approaches and compared the results with matched Illumina microarray and SRS data. We assessed sequencing quality metrics, variant detection performance for single nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs), while examining the impact of sequencing depth, read length, and multiplexing.ONT LRS demonstrated competitive performance with SRS for SNV detection, particularly in low complexity regions (F-measure: 0.763 vs 0.770), though with slightly lower performance in high complexity regions (F-measure: 0.954 vs 0.968). For indel detection, LRS showed robust performance in high complexity regions (F-measure: 0.850) which, however, decreased in low complexity regions (F-measure: 0.453). LRS identified 2.82 times more structural variants than SRS and detected variants across a broader size range (8 bp to 129 Mb vs 2 bp to 6 kb). Sequencing depth strongly correlated with variant calling performance across all variant types, with correlation coefficients of 0.80 for SNVs in high complexity regions, 0.84 for SNVs in low complexity regions, and exceeding 0.9 for indels. SV detection in LRS showed strong depth dependence (r = 0.939), while SRS SV calls remained stable across depths.Our findings demonstrate that ONT LRS complements existing sequencing technologies, offering advantages in detecting structural variants and analysing low complexity regions, while maintaining competitive performance in standard variant detection. This study provides practical insights for optimising ONT sequencing strategies and highlights areas for future methodological improvement.