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Author(s): Wenger, Aaron and Hickey, Luke and Chin, Jason and Korlach, Jonas

Despite amazing progress over the past quarter century in the technology to detect genetic variants, intermediate-sized structural variants (50 bp to 50 kb) have remained difficult to identify. Such variants are too small to detect with array comparative genomic hybridization, but too large to reliably discover with short-read DNA sequencing. Recent de novo assemblies of human genomes have demonstrated the power of PacBio Single Molecule, Real-Time (SMRT) Sequencing to fill this technology gap and sensitively identify structural variants in the human genome. While de novo assembly is the ideal method to identify variants in a genome, it requires high depth of coverage. A structural variant discovery approach that utilizes lower coverage would facilitate evaluation of large patient and population cohorts. Here we introduce such an approach and apply it to 10-fold coverage of several human genomes generated on the PacBio Sequel System. To identify structural variants in low-fold coverage whole genome sequencing data, we apply a reference-based, re-sequencing workflow. First, reads are mapped to the human reference genome with a local aligner. The local alignments often end at structural variant loci. To connect co-linear local alignments across structural variants, we apply a novel algorithm that merges alignments into “chains” and refines the alignment edges. Then, the chained alignments are scanned for windows with an excess of insertions or deletions to identify candidate structural variant loci. Finally, the read support at each putative variant locus is evaluated to produce a variant call. Single nucleotide information is incorporated to phase and evaluate the zygosity of each structural variant. In 10-fold coverage human genome sequence, we identify the vast majority of the structural variants found by de novo assembly, thus demonstrating the power of low-fold coverage SMRT Sequencing to affordably and effectively detect structural variants.

Organization: PacBio
Year: 2017

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