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Introduction: Puumala virus (family Hantaviridae , genus Orthohantavirus ) is distributed in most regions of the European part of Russia. However, information about its genetic variants circulating on the territory of the Central Federal District is extremely scarce., Materials and Methods: Rodents' tissue samples were tested after reverse transcription by PCR for the presence of hantaviral RNA. The amplified fragments of the L segment were sequenced by the Sanger method. For two samples, sequences of all three segments were obtained using the NGS method. Phylogenetic trees were built in the MEGA-X software., Results: Puumala virus was found in six samples. Based on the phylogenetic analysis of sequences of three segments, the obtained genetic variants belong to the sublineage previously designated as W-RUS., Conclusion: A genetic variant of the Puumala virus, belonging to the subline W-RUS, circulates on the territory of the Volokolamsk district of Moscow region.

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

Introduction: Since the outbreak of the COVID-19 pandemic caused by SARS-CoV-2 novel coronavirus, the international community has been concerned about the emergence of mutations altering some biological properties of the pathogen like increasing its infectivity or virulence. Particularly, since the end of 2020, several variants of concern have been identified around the world, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). However, the existing mechanism of detecting important mutations are not always effective enough, since only a relatively small part of all pathogen samples can be examined by whole genome sequencing due to its high cost., Material and Methods: In this study, we have designed special primer panel and used it for targeted highthroughput sequencing of several significant S-gene (spike) regions of SARS-CoV-2. The Illumina platform averaged approximately 50,000 paired-end reads with a length of ≥150 bp per sample. This method was used to examine 579 random samples obtained from COVID-19 patients in Moscow and the Moscow region from February to June 2021., Results: This study demonstrated the dynamics of distribution of several SARS-CoV-2 strains and its some single mutations. It was found that the Delta strain appeared in the region in May 2021, and became prevalent in June, partially displacing other strains., Discussion: The obtained results provide an opportunity to assign the viral samples to one of the strains, including the previously mentioned in time- and cost-effective manner. The approach can be used for standardization of the procedure of searching for mutations in individual regions of the SARS-CoV-2 genome. It allows to get a more detailed data about the epidemiological situation in a region.