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.

Nowadays, a new type of algorithms inspired by the quantum theory has arisen and brought to light new views of solving standard computational problems like searching, optimizing, scheduling etc. Some of the principles of quantum mechanics can be used even if a quantum computer is not available. The focus of this article is on the utilization of a quantum approach in a selected algorithm called DSOMA. A quantum computing based algorithm is described and all suggested improvements are demonstrated on the flowshop optimization problem. The novelty of the proposed algorithm consists in the use of quantum data. Individuals in other algorithms are usually encoded into a quantum state, whereas the individuals in our extension are just enriched by quantum information, and this data is used to improve behavior of the algorithm. Based on the outcomes of experiments carried out within our research the improved algorithm has achieved better results.

Parallel computing can be very useful for meta-heuristic algorithms, because it can reduce time to find a solution with given properties. On the other hand parallel programming is generally more difficult than the sequential one and we need more development time to get a working parallel solution. We are developing a tool named Kaira which is intended for modelling, debugging, and automated generation of parallel applications. The modelling is based on our own variant of Coloured Petri Nets. In this paper we present how to create parallel programs from an existing sequential implementation of Ant Colony Optimization Algorithm using Kaira. This approach simplifies the development process and thus saves the development time and costs.