![]() The NanoVar workflow is a series of processes that utilizes 3GS long reads to discover and characterize SVs in DNA samples. When applied to patient data, we demonstrated the feasibility and speed of implementing the NanoVar workflow for SV discovery in low-depth 3GS clinical samples. In this manuscript, we evaluated NanoVar’s SV detection precision and recall among other tools using simulation datasets and real data. It is optimized to work with shallow long-read WGS data at a minimum sequencing depth of 4X (12 gigabases (Gb)) for homozygous SVs and 8X (24 Gb) for heterozygous SVs, which can be achieved with one to ten ONT MinION sequencing runs, depending on the flowcell chemistry, library preparation kit, and sample quality. NanoVar adopts a neural-network-based algorithm for high-confident SV detection and SV zygosity estimation for all SV classes. To overcome these issues, we developed a new SV caller tool, NanoVar, which utilizes low-depth Oxford Nanopore Technologies (ONT) WGS data for accurate SV characterization in patients. Despite better SV detection capabilities, the low throughput and high sequencing cost per megabase of 3GS obstruct its feasibility to be used in routine SV interrogation in patients. On the other hand, longer read lengths (> 1 kb) reduce mapping ambiguity, resolve repetitive sequences and complex SVs, and discover a much larger extent of SVs than short reads. This is mostly due to the inadequacy of short reads (50–200 bp) to elucidate large genomic variations also involving novel sequence insertions or repetitive elements, which may give rise to high false discovery rates. In the domain of SV discovery, many groups have reported that 3GS approaches provided higher SV detection sensitivity and resolution than 2GS, despite their higher sequencing error rate. While 3GS is currently mainly restricted to the study of small genomes or targeted sequencing, recent studies have reported mammalian whole-genome sequencing (WGS) but at a higher sequencing cost per megabase as compared to the older technologies. Although 3GS technologies were made accessible to a large audience, it has not yet supplanted 2GS technologies due to its higher sequencing error rate and lower throughput. There are currently two main standards of sequencing-based methods for comprehensive SV detection: long-read or third-generation sequencing (3GS) and short-read or second-generation sequencing (2GS). As the clinical impacts of SVs continue to unveil, there is a clear need for accurate, rapid, and inexpensive workflows for routine SV profiling in patients to expedite biomarker discovery and broaden clinical investigations. Over the years, disease-associated SVs were indicated as biomarkers for diagnosis, prognosis, and therapy guidance for patients, which could be screened through sequencing-based and non-sequencing-based methods in clinics. SVs can exist as different classes including deletion, duplication, insertion, inversion, and translocation. ![]() Structural variants (SVs), defined as genomic alterations greater than 50 base pairs (bp), can functionally affect cellular physiology by forming genetic lesions which may lead to gene dysregulation or novel gene fusions, driving the development of diseases such as cancer, Mendelian disorders, and complex diseases. Structural variations are implicated in the development of many human diseases and account for most of the genetic variations by means of nucleotides in the human population. ![]()
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