Comparison of structural variants detected by optical mapping with long-read next-generation sequencing

Author:

Savara Jakub12ORCID,Novosád Tomáš1ORCID,Gajdoš Petr1ORCID,Kriegová Eva2ORCID

Affiliation:

1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, 708 00, Czech Republic

2. Department of Immunology, Faculty of Medicine and Dentistry, Palacký University in Olomouc and University Hospital Olomouc, 779 00, Olomouc, Czech Republic

Abstract

Abstract Motivation Recent studies have shown the potential of using long-read whole-genome sequencing (WGS) approaches and optical mapping (OM) for the detection of clinically relevant structural variants (SVs) in cancer research. Three main long-read WGS platforms are currently in use: Pacific Biosciences (PacBio), Oxford Nanopore Technologies (ONT) and 10x Genomics. Recently, whole-genome OM technology (Bionano Genomics) has been introduced into human diagnostics. Questions remain about the accuracy of these long-read sequencing platforms, how comparable/interchangeable they are when searching for SVs and to what extent they can be replaced or supplemented by OM. Moreover, no tool can effectively compare SVs obtained by OM and WGS. Results This study compared optical maps of the breast cancer cell line SKBR3 with AnnotSV outputs from WGS platforms. For this purpose, a software tool with comparative and filtering features was developed. The majority of SVs up to a 50 kbp distance variance threshold found by OM were confirmed by all WGS platforms, and ∼99% of translocations and ∼80% of deletions found by OM were confirmed by both PacBio and ONT, with ∼70% being confirmed by 10x Genomics in combination with PacBio and/or ONT. Interestingly, long deletions (>100 kbp) were detected only by 10x Genomics. Regarding insertions, ∼74% was confirmed by PacBio and ONT, but none by 10x Genomics. Inversions and duplications detected by OM were not detected by WGS. Moreover, the tool enabled the confirmation of SVs that overlapped in the same gene(s) and was applied to the filtering of disease-associated SVs. Availability and implementation https://github.com/novosadt/om-annotsv-svc.

Funder

Ministry of Health of the Czech Republic

Internal Grant Agency of Palacký University

VSB-Technical University of Ostrava

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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