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.

The current development in the Next-Generation Sequencing (NGS) technologies and the gradual growth of its use leads to the production of a huge amount of sequencing data. There is a need to efficiently transfer and store these data. This article introduces a novel lossy transformation algorithm for quality scores in sequencing data. Asymptotically, the algorithm preserves the likelihood of occurrence of particular quality score in individual positions of quality sequences. Such a model may be advantageous for sequencing data with very high coverage, such as targeted amplicon sequencing data. In experimental results, we show the comparison of characteristics of this algorithm with other algorithms performing lossy compression of the quality sequences. The proposed algorithm can be easily integrated into current sequencing pipelines. In this work we apply the algorithm to SAM files, which are then compressed into BAM files. The goal of the algorithm is to modify the data so that the subsequent Deflate algorithm application achieves a better compression ratio while minimizing the negative effects in the subsequent variant calling.

Context transformations is a very simple data transformation method that we presented recently and it is used to decrease uncertainty in input data. The transformation is based on exchange of two different di-grams. This paper is focused on new consequences of the relationships discovered subsequently. We were able to find a mathematical model which predicts the efficiency of each transformation. The new type of the transformation, Generalized context transformation, developed recently is more efficient than the previous one and it is able to remove almost all redundancy based on the symbols mutual information. The newly developed algorithm is computationally and entropic ally more efficient than the previous one.

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