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Motivation: Detection of structural variants (SV) from the alignment ofsample DNA reads to the reference genome is an important problem inunderstanding human diseases. Long reads that can span repeat regions, alongwith an accurate alignment of these long reads play an important role inidentifying novel SVs. Long read sequencers such as nanopore sequencing canaddress this problem by providing very long reads but with high error rates,making accurate alignment challenging. Many errors induced by nanoporesequencing have a bias because of the physics of the sequencing process andproper utilization of these error characteristics can play an important role indesigning a robust aligner for SV detection problems. In this paper, we designand evaluate HQAlign, an aligner for SV detection using nanopore sequencedreads. The key ideas of HQAlign include (i) using basecalled nanopore readsalong with the nanopore physics to improve alignments for SVs (ii)incorporating SV specific changes to the alignment pipeline (iii) adaptingthese into existing state-of-the-art long read aligner pipeline, minimap2(v2.24), for efficient alignments.  Results: We show that HQAlign captures about 4%-6% complementary SVs acrossdifferent datasets which are missed by minimap2 alignments while having astandalone performance at par with minimap2 for real nanopore reads data. Forthe common SV calls between HQAlign and minimap2, HQAlign improves the startand the end breakpoint accuracy for about 10%-50% of SVs across differentdatasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap285.64% for nanopore reads alignment to recent telomere-to-telomere CHM13assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment toGRCh37 human genome.

HQAlign: Aligning nanopore reads for SV detection using current-level modeling