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The antibiotic resistance genes (ARGs) limit the susceptibility of bacteria to antimicrobials, representing a problem of high importance. Current research on the presence of ARGs in microorganisms focuses mainly on humans, livestock, hospitals, or wastewater. However, the spectrum of ARGs in the dust resistome in workplaces and households has gone relatively unexplored. This pilot study aimed to analyze resistome in indoor dust samples from participants' workplaces (a pediatric hospital, a maternity hospital, and a research center) and households and compare two different approaches to the ARGs analysis; high-throughput quantitative PCR (HT-qPCR) and whole metagenome shotgun sequencing (WMGS). In total, 143 ARGs were detected using HT-qPCR, with ARGs associated with the macrolides, lincosamides, and streptogramin B (MLSB) phenotype being the most abundant, followed by MDR (multi-drug resistance) genes, and genes conferring resistance to aminoglycosides. A higher overall relative quantity of ARGs was observed in indoor dust samples from workplaces than from households, with the pediatric hospital being associated with the highest relative quantity of ARGs. WMGS analysis revealed 36 ARGs, of which five were detected by both HT-qPCR and WMGS techniques. Accordingly, the efficacy of the WMGS approach to detect ARGs was lower than that of HT-qPCR. In summary, our pilot data revealed that indoor dust in buildings where people spend most of their time (workplaces, households) can be a significant source of antimicrobial-resistant microorganisms, which may potentially pose a health risk to both humans and animals. [ABSTRACT FROM AUTHOR]

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS. [ABSTRACT FROM AUTHOR]

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals--squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing. [ABSTRACT FROM AUTHOR]

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g., local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also non-coding regions are examined. [ABSTRACT FROM AUTHOR]