Determining optical mapping errors by simulations

• Published in: Bioinformatics (Oxford, England) Vol. 37; no. 20; pp. 3391 - 3397
• Main Authors: Vašinek, Michal, Běhálek, Marek, Gajdoš, Petr, Fillerová, Regina, Kriegová, Eva
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 25-10-2021
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btab259

Abstract
Motivation
Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system.
Results
The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework’s main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis.
Availability and implementation
Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/.
Supplementary information
Supplementary data are available at Bioinformatics online.