TY - JOUR
T1 - Investigating the performance of Oxford Nanopore long-read sequencing with respect to Illumina microarrays and short-read sequencing
AU - Santos, Renato
AU - Lee, Hyunah
AU - Williams, Alexander
AU - Baffour-Kyei, Anastasia
AU - Lee, Sang-Hyuck
AU - Troakes, Claire
AU - Al-Chalabi, Ammar
AU - Breen, Gerome
AU - Iacoangeli, Alfredo
PY - 2025/5/8
Y1 - 2025/5/8
N2 - Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) has emerged as a promising genomic analysis tool, yet comprehensive benchmarks with established platforms across diverse datasets remain limited. This study aimed to benchmark LRS performance against Illumina short-read sequencing (SRS) and microarrays for variant detection across different genomic contexts and to evaluate the impact of experimental factors. We sequenced 14 human genomes using the three platforms and evaluated single nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs) detection, stratifying by high-complexity, low-complexity, and dark genome regions, while assessing effects of multiplexing, depth, and read length. LRS SNV accuracy was slightly lower than SRS in high-complexity regions (F-measure: 0.954 vs. 0.967) but showed comparable sensitivity in low-complexity regions. LRS showed robust performance for small (1-5bp) indels in high-complexity regions (F-measure: 0.869), but SRS agreement decreased significantly in low-complexity regions and larger indel sizes. Within dark regions, LRS identified more indels than SRS, but showed lower base-level accuracy. LRS identified 2.86 times more SVs than SRS, excelling at detecting large variants (>6kb), with SV detection improving with sequencing depth. Sequencing depth strongly influenced variant calling performance, whereas multiplexing effects were minimal. Our findings provide valuable insights for optimising LRS applications in genomic research and diagnostics.
AB - Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) has emerged as a promising genomic analysis tool, yet comprehensive benchmarks with established platforms across diverse datasets remain limited. This study aimed to benchmark LRS performance against Illumina short-read sequencing (SRS) and microarrays for variant detection across different genomic contexts and to evaluate the impact of experimental factors. We sequenced 14 human genomes using the three platforms and evaluated single nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs) detection, stratifying by high-complexity, low-complexity, and dark genome regions, while assessing effects of multiplexing, depth, and read length. LRS SNV accuracy was slightly lower than SRS in high-complexity regions (F-measure: 0.954 vs. 0.967) but showed comparable sensitivity in low-complexity regions. LRS showed robust performance for small (1-5bp) indels in high-complexity regions (F-measure: 0.869), but SRS agreement decreased significantly in low-complexity regions and larger indel sizes. Within dark regions, LRS identified more indels than SRS, but showed lower base-level accuracy. LRS identified 2.86 times more SVs than SRS, excelling at detecting large variants (>6kb), with SV detection improving with sequencing depth. Sequencing depth strongly influenced variant calling performance, whereas multiplexing effects were minimal. Our findings provide valuable insights for optimising LRS applications in genomic research and diagnostics.
KW - Oxford Nanopore Technologies
KW - Long-read sequencing
KW - Short-read sequencing
KW - Variant calling
KW - Benchmark
KW - Genomic variants
KW - Low-complexity regions
KW - Multiplexing
KW - Experimental variables
U2 - 10.1101/2024.12.19.629409
DO - 10.1101/2024.12.19.629409
M3 - Article
SN - 1422-0067
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
ER -
