SnakeLines: integrated set of computational pipelines for sequencing reads.

With the rapid growth of massively parallel sequencing technologies, still more laboratories are utilising sequenced DNA fragments for genomic analyses. Interpretation of sequencing data is, however, strongly dependent on bioinformatics processing, which is often too demanding for clinicians and researchers without a computational background. Another problem represents the reproducibility of computational analyses across separated computational centres with inconsistent versions of installed libraries and bioinformatics tools. We propose an easily extensible set of computational pipelines, called SnakeLines, for processing sequencing reads; including mapping, assembly, variant calling, viral identification, transcriptomics, and metagenomics analysis. Individual steps of an analysis, along with methods and their parameters can be readily modified in a single configuration file. Provided pipelines are embedded in virtual environments that ensure isolation of required resources from the host operating system, rapid deployment, and reproducibility of analysis across different Unix-based platforms. SnakeLines is a powerful framework for the automation of bioinformatics analyses, with emphasis on a simple set-up, modifications, extensibility, and reproducibility. The framework is already routinely used in various research projects and their applications, especially in the Slovak national surveillance of SARS-CoV-2.

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations.

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/ .

WarpSTR: determining tandem repeat lengths using raw nanopore signals.

MOTIVATION: Short tandem repeats (STRs) are regions of a genome containing many consecutive copies of the same short motif, possibly with small variations. Analysis of STRs has many clinical uses but is limited by technology mainly due to STRs surpassing the used read length. Nanopore sequencing, as one of long-read sequencing technologies, produces very long reads, thus offering more possibilities to study and analyze STRs. Basecalling of nanopore reads is however particularly unreliable in repeating regions, and therefore direct analysis from raw nanopore data is required. RESULTS: Here, we present WarpSTR, a novel method for characterizing both simple and complex tandem repeats directly from raw nanopore signals using a finite-state automaton and a search algorithm analogous to dynamic time warping. By applying this approach to determine the lengths of 241 STRs, we demonstrate that our approach decreases the mean absolute error of the STR length estimate compared to basecalling and STRique. AVAILABILITY AND IMPLEMENTATION: WarpSTR is freely available at https://github.com/fmfi-compbio/warpstr.

Mitochondrial DNA copy number changes, heteroplasmy, and mutations in plasma-derived exosomes and brain tissue of glioblastoma patients.

Glioblastoma is the most common malignant tumor of the central nervous system (CNS) in adults. Glioblastoma cells show increased glucose consumption associated with poor prognosis. Since mitochondria play a crucial role in energy metabolism, mutations and copy number changes of mitochondrial DNA may serve as biomarkers. As the brain is difficult to access, analysis of mitochondria directly from the brain tissue represents a challenge. Exosome analysis is an alternative (still poorly explored) approach to investigate molecular changes in CNS tumors. We analyzed brain tissue DNA and plasma-derived exosomal DNA (exoDNA) of 44 glioblastoma patients and 40 control individuals. Quantitative real-time PCR was performed to determine mtDNA copy numbers and the Kruskal-Wallis and Mann-Whitney U test were used for statistical analysis of data. Subsequently, sequencing libraries were prepared and sequenced on the MiSeq platform to identify mtDNA point mutations. Tissue mtDNA copy number was different among controls and patients in multiple comparisons. A similar tendency was detected in exosomes. Based on NGS analysis, several mtDNA point mutations showed slightly different frequencies between cases and controls, but the clinical relevance of these observations is difficult to assess and likely less than that of overall mtDNA copy number changes. Allele frequencies of variants were used to determine the level of heteroplasmy (found to be higher in exo-mtDNA of control individuals). Despite the suggested potential, the use of such biomarkers for the screening and/or diagnosis of glioblastomas is still limited, thus further studies are needed.

Systematic Genomic Surveillance of SARS-CoV-2 Virus on Illumina Sequencing Platforms in the Slovak Republic-One Year Experience.

To explore a genomic pool of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the pandemic, the Ministry of Health of the Slovak Republic formed a genomics surveillance workgroup, and the Public Health Authority of the Slovak Republic launched a systematic national epidemiological surveillance using whole-genome sequencing (WGS). Six out of seven genomic centers implementing Illumina sequencing technology were involved in the national SARS-CoV-2 virus sequencing program. Here we analyze a total of 33,024 SARS-CoV-2 isolates collected from the Slovak population from 1 March 2021, to 31 March 2022, that were sequenced and analyzed in a consistent manner. Overall, 28,005 out of 30,793 successfully sequenced samples met the criteria to be deposited in the global GISAID database. During this period, we identified four variants of concern (VOC)-Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2) and Omicron (B.1.1.529). In detail, we observed 165 lineages in our dataset, with dominating Alpha, Delta and Omicron in three major consecutive incidence waves. This study aims to describe the results of a routine but high-level SARS-CoV-2 genomic surveillance program. Our study of SARS-CoV-2 genomes in collaboration with the Public Health Authority of the Slovak Republic also helped to inform the public about the epidemiological situation during the pandemic.