Biology subjects

Lukashev, A. N.

Publications and source records attributed to Lukashev, A. N..

3 recordsLinked to original sources

bioRxiv2026

Evaluating Reference-Independent Pipelines for the Detection of Spreading Organisms in Metagenomic Datasets

The emergence of unidentified pathogens, or "Disease X," poses a significant threat to global health, necessitating the development of proactive surveillance strategies for the wildlife and human virosphere. Since novel viruses often lack universal genetic markers or known homologs, this study evaluates four reference-independent computational pipelines: coverage-based, k-mer-based, nucleotide clustering, and Large Language Model (LLM)-based designed to detect spreading organisms by comparing distinct metagenomic datasets. Using a real-world pandemic dataset of human nasopharyngeal RNA-seq runs and a semi-synthetic dataset enriched with divergent Egovirales sequences, we measured the sensitivity, selectivity, and computational efficiency of each approach. The coverage-based method proved most robust, consistently achieving 100% genome coverage of SARS-CoV-2 and maintaining high selectivity even at low viral concentrations, though it required extensive computational resources (20 days of CPU time for 2B reads). In contrast, the k-mer-based approach offered a tenfold reduction in execution time and high selectivity but was sensitive to data depletion, failing to detect targets at very low abundances. The clustering-based pipeline performed effectively at moderate concentrations but suffered from sequence fragmentation in sparse data, while the LLM-based method (using ViraLM), despite its efficiency, exhibited critically low selectivity due to current latent space partitioning limitations. These results demonstrate that while k-mer and LLM-based tools provide rapid screening capabilities, the coverage-based approach remains the most reliable for sensitive pathogen discovery. Ultimately, these reference-independent workflows are essential for illuminating metagenomic "dark matter" and establishing early warning systems for emerging infectious diseases

bioinformatics↗

bioRxiv2020

At least seven distinct rotavirus genotype constellations in bats with evidence of reassortment and zoonotic transmissions

Bats host many viruses pathogenic to humans, and increasing evidence suggests that Rotavirus A (RVA) also belongs to this list. Rotaviruses cause diarrheal disease in many mammals and birds, and their segmented genomes allow them to reassort and increase their genetic diversity. Eighteen out of 2,142 bat fecal samples (0.8%) collected from Europe, Central America and Africa were PCR-positive for RVA and 11 of those were fully characterized using viral metagenomics. Upon contrasting their genomes with publicly available data, at least 7 distinct bat RVA genotype constellations (GCs) were identified, including evidence of reassortments and 6 novel genotypes. Some of these constellations are spread across the world, whereas others appear to be geographically restricted. Our analyses also suggest that several unusual human and equine RVA strains might be of bat RVA origin, based on their phylogenetic clustering, despite varying levels of nucleotide sequence identities between them. Although SA11 is one of the most widely used reference strains for RVA research and forms the backbone of a reverse genetics system, its origin remained enigmatic. Remarkably, the majority of the genotypes of SA11-like strains were shared with Gabonese bat RVAs, suggesting a potential common origin. Overall, our findings suggest an underexplored genetic diversity of RVAs in bats, which is likely only the tip of the iceberg. Increasing contact between humans and bat wildlife will further increase the zoonosis risk, which warrants closer attention to these viruses. ImportanceThe increased research on bat coronaviruses after SARS-CoV and MERS-CoVallowed the very rapid identification of SARS-CoV-2. This is an excellent example of the importance of knowing viruses harbored by wildlife in general and bats in particular, for global preparedness against emerging viral pathogens. The current effort to characterize bat rotavirus strains from 3 continents shed light on the vast genetic diversity of rotaviruses and also hinted at a bat origin for several atypical rotaviruses in humans and animals, implying that zoonoses of bat rotaviruses might occur more frequently than currently realized.

microbiology↗

bioRxiv2018

Computational Discovery of Evolutionary Conserved Enterovirus 71 RNA Secondary Structures

Viral RNAs store information in their sequence and structure. Little, however is known about the contribution of RNA structure to viral replication and evolution. We previously described RNA ISRAEU to computationally identify structured regions in viral RNA based on evolutionary conservation of predicted structures. Here, we apply RNA GUESS (Generator of Uniformly Evolved Secondary Structures) to better understand enterovirus 71, a highly pathogenic human picornavirus. RNA GUESS identified previously known evolutionarily conserved picornavirus RNA structures, and uniquely predicted RNA structures with significant structural conservation despite the lack of an obvious sequence conservation. One structure consists of a stretch of obligatorily unpaired nucleotides that can potentially interact with RNA-binding proteins and siRNAs. Another, forms a potential RNA switch that can form two alternative structures while the third and fourth constitute hairpin-like structures. These findings provide a launching point for physical and genetic studies regarding the function and structures of these RNA elements.

bioinformatics↗