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Abstract 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/ .

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.