Altered sequencing success at non-B-DNA motifs

Abstract

Modern sequencing technologies are not error-free, and might have elevated error rates at some locations of the genome. A potential cause for such elevated error rates is the formation of alternative DNA structures (non-B DNA), such as G-quadruplexes (G4s), Z-DNA, or cruciform structures, during sequencing. Approximately 13% of the human genome has the potential to form such structures, which have been previously shown to affect the activity of DNA polymerases and helicases. Here we tested whether motifs with the potential to form non-B DNA (non-B motifs) influence the sequencing success of three major sequencing technologies--Illumina, Pacific Biosciences (PacBio) HiFi, and Oxford Nanopore Technologies (ONT). We estimated sequencing success by computing the rates of single-nucleotide, insertion, and deletion errors, as well as by evaluating mean read depth and mean base quality. Overall, all technologies exhibited altered sequencing success for most non-B motif types. Single-nucleotide error rates were generally increased for G-quadruplexes (G4s) and Z-DNA motifs in all three technologies. Illumina and PacBio HiFi deletion error rates were also increased for all non-B types except for Z-DNA motifs, while in ONT they were increased substantially only for G4 motifs. Insertion error rates for non-B motifs were highly elevated in Illumina, moderately elevated in PacBio HiFi, and only slightly elevated in ONT. Using Poisson regression modeling, we evaluated how non-B DNA motifs and other factors influence sequencing error profiles. Using the error rates at non-B motifs, we developed a probabilistic approach to determine the number of false-positive single-nucleotide variants (SNVs) in different sample size and variant frequency cutoff scenarios, as well as in previously generated sequencing data sets (1000Genomes, Simons Genome Diversity Project, and gnomAD). Overall, the effect of non-B DNA on sequencing should be considered in downstream analyses, particularly in studies with limited read depth--e.g., single-cell and ancient DNA sequencing, as well as sequencing of pooled population samples--and when scoring variants with low frequency (e.g., singletons). Because each sequencing technology analyzed has a unique error profile at non-B motifs, a combination of different technologies should be considered in future sequencing studies of such motifs, to maximize accuracy.