A global metagenomic map of urban microbiomes and antimicrobial resistance.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

Modular binder technology by NGS-aided, high-resolution selection in yeast of designed armadillo modules.

Establishing modular binders as diagnostic detection agents represents a cost- and time-efficient alternative to the commonly used binders that are generated one molecule at a time. In contrast to these conventional approaches, a modular binder can be designed in silico from individual modules to, in principle, recognize any desired linear epitope without going through a selection and hit-validation process, given a set of preexisting, amino acid-specific modules. Designed armadillo repeat proteins (dArmRP) have been developed as modular binder scaffolds, and we report here the generation of highly specific dArmRP modules by yeast surface display selection, performed on a rationally designed dArmRP library. A selection strategy was developed to distinguish the binding difference resulting from a single amino acid mutation in the target peptide. Our reverse-competitor strategy introduced here employs the designated target as a competitor to increase the sensitivity when separating specific from cross-reactive binders that show similar affinities for the target peptide. With this switch in selection focus from affinity to specificity, we found that the enrichment during this specificity sort is indicative of the desired phenotype, regardless of the binder abundance. Hence, deep sequencing of the selection pools allows retrieval of phenotypic hits with only 0.1% abundance in the selectivity sort pool from the next-generation sequencing data alone. In a proof-of-principle study, a binder was created by replacing all corresponding wild-type modules with a newly selected module, yielding a binder with very high affinity for the designated target that has been successfully validated as a detection agent in western blot analysis.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

Genomics in the long-read sequencing era.

Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.

Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges.

Next-generation sequencing (NGS) has revolutionized the field of rare disease diagnostics. Whole exome and whole genome sequencing are now routinely used for diagnostic purposes; however, the overall diagnosis rate remains lower than expected. In this work, we review current approaches used for calling and interpretation of germline genetic variants in the human genome, and discuss the most important challenges that persist in the bioinformatic analysis of NGS data in medical genetics. We describe and attempt to quantitatively assess the remaining problems, such as the quality of the reference genome sequence, reproducible coverage biases, or variant calling accuracy in complex regions of the genome. We also discuss the prospects of switching to the complete human genome assembly or the human pan-genome and important caveats associated with such a switch. We touch on arguably the hardest problem of NGS data analysis for medical genomics, namely, the annotation of genetic variants and their subsequent interpretation. We highlight the most challenging aspects of annotation and prioritization of both coding and non-coding variants. Finally, we demonstrate the persistent prevalence of pathogenic variants in the coding genome, and outline research directions that may enhance the efficiency of NGS-based disease diagnostics.

Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny.

This study proposes a novel approach to studying severe acute respiratory syndrome coronavirus 2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology, across multiple simulated datasets, accurately reflected the known mutations with an average accuracy improvement of 20% over the consensus method. In real-world tests using data from GISAID and NCBI-SRA, our approach demonstrated an increase in reliability by reducing the error margin by approximately 15%. The genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology.

ALK fusion NSCLC oncogenes promote survival and inhibit NK cell responses via SERPINB4 expression.

Anaplastic lymphoma kinase (ALK) fusion variants in Non-Small Cell Lung Cancer (NSCLC) consist of numerous dimerizing fusion partners. Retrospective investigations suggest that treatment benefit in response to ALK tyrosine kinase inhibitors (TKIs) differs dependent on the fusion variant present in the patient tumor. Therefore, understanding the oncogenic signaling networks driven by different ALK fusion variants is important. To do this, we developed controlled inducible cell models expressing either Echinoderm Microtubule Associated Protein Like 4 (EML4)-ALK-V1, EML4-ALK-V3, Kinesin Family Member 5B (KIF5B)-ALK, or TRK-fused gene (TFG)-ALK and investigated their transcriptomic and proteomic responses to ALK activity modulation together with patient-derived ALK-positive NSCLC cell lines. This allowed identification of both common and isoform-specific responses downstream of these four ALK fusions. An inflammatory signature that included upregulation of the Serpin B4 serine protease inhibitor was observed in both ALK fusion inducible and patient-derived cells. We show that Signal transducer and activator of transcription 3 (STAT3), Nuclear Factor Kappa B (NF-κB) and Activator protein 1 (AP1) are major transcriptional regulators of SERPINB4 downstream of ALK fusions. Upregulation of SERPINB4 promotes survival and inhibits natural killer cell-mediated cytotoxicity, which has potential for therapeutic impact targeting the immune response together with ALK TKIs in NSCLC.

CHEK2 germline variants identified in familial nonmedullary thyroid cancer lead to impaired protein structure and function.

Approximately 5 to 15% of nonmedullary thyroid cancers (NMTC) present in a familial form (familial nonmedullary thyroid cancers [FNMTC]). The genetic basis of FNMTC remains largely unknown, representing a limitation for diagnostic and clinical management. Recently, germline mutations in DNA repair-related genes have been described in cases with thyroid cancer (TC), suggesting a role in FNMTC etiology. Here, two FNMTC families were studied, each with two members affected with TC. Ninety-four hereditary cancer predisposition genes were analyzed through next-generation sequencing, revealing two germline CHEK2 missense variants (c.962A > C, p.E321A and c.470T > C, p.I157T), which segregated with TC in each FNMTC family. p.E321A, located in the CHK2 protein kinase domain, is a rare variant, previously unreported in the literature. Conversely, p.I157T, located in CHK2 forkhead-associated domain, has been extensively described, having conflicting interpretations of pathogenicity. CHK2 proteins (WT and variants) were characterized using biophysical methods, molecular dynamics simulations, and immunohistochemistry. Overall, biophysical characterization of these CHK2 variants showed that they have compromised structural and conformational stability and impaired kinase activity, compared to the WT protein. CHK2 appears to aggregate into amyloid-like fibrils in vitro, which opens future perspectives toward positioning CHK2 in cancer pathophysiology. CHK2 variants exhibited higher propensity for this conformational change, also displaying higher expression in thyroid tumors. The present findings support the utility of complementary biophysical and in silico approaches toward understanding the impact of genetic variants in protein structure and function, improving the current knowledge on CHEK2 variants' role in FNMTC genetic basis, with prospective clinical translation.

Profiling of RNA editing events in plant organellar transcriptomes with high-throughput sequencing.

RNA editing is a crucial post-transcriptional modification process in plant organellar RNA metabolism. rRNA removal-based total RNA-seq is one of the most common methods to study this event. However, the lack of commercial kits to remove rRNAs limits the usage of this method, especially for non-model plant species. DSN-seq is a transcriptome sequencing method utilizing duplex-specific nuclease (DSN) to degrade highly abundant cDNA species especially those from rRNAs while keeping the robustness of transcript levels of the majority of other mRNAs, and has not been applied to study RNA editing in plants before. In this study, we evaluated the capability of DSN-seq to reduce rRNA content and profile organellar RNA editing events in plants, as well we used commercial Ribo-off-seq and standard mRNA-seq as comparisons. Our results demonstrated that DSN-seq efficiently reduced rRNA content and enriched organellar transcriptomes in rice. With high sensitivity to RNA editing events, DSN-seq and Ribo-off-seq provided a more complete and accurate RNA editing profile of rice, which was further validated by Sanger sequencing. Furthermore, DSN-seq also demonstrated efficient organellar transcriptome enrichment and high sensitivity for profiling RNA editing events in Arabidopsis thaliana. Our study highlights the capability of rRNA removal-based total RNA-seq for profiling RNA editing events in plant organellar transcriptomes and also suggests DSN-seq as a widely accessible RNA editing profiling method for various plant species.

VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data.

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.

Generative Haplotype Prediction Outperforms Statistical Methods for Small Variant Detection in NGS Data.

MOTIVATION: Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov Models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false positive detections in a typical human whole genome, creating a significant manual review burden. RESULTS: We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern Large Language Models (LLMs). We train our model on 37 Whole Genome Sequences (WGS) from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3 and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested. AVAILABILITY AND IMPLEMENTATION: Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code.

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

Whole-exome sequencing to identify causative variants in juvenile sudden cardiac death.

BACKGROUND: Juvenile sudden cardiac death (SCD) remains unexplained in approximately 40% of cases, leading to a significant emotional burden for the victims' families and society. Comprehensive investigations are essential to uncover its elusive causes and enable cascade family screening. This study aimed to enhance the identification of likely causative variants in juvenile SCD cases (age ≤ 50 years), particularly when autopsy findings are inconclusive. RESULTS: Autopsy revealed diagnostic structural abnormalities in 46%, non-diagnostic findings in 23%, and structurally normal hearts in 31% of cases. Whole-exome sequencing (WES), refined through a customized virtual gene panel was used to identify variants. These variants were then evaluated using a multidisciplinary approach and a structured variant prioritization scheme. Our extended approach identified likely causative variants in 69% of cases, outperforming the diagnostic yields of both the cardio panel and standard susceptibility gene analysis (50% and 16%, respectively). The extended cardio panel achieved an 80% diagnostic yield in cases with structurally normal hearts, demonstrating its efficacy in challenging scenarios. Notably, half of the positive cases harboured a single variant, while the remainder had two or more variants. CONCLUSION: This study highlights the efficacy of a multidisciplinary approach employing WES and a tailored virtual gene panel to elucidate the aetiology of juvenile SCD. The findings support the expansion of genetic testing using tailored gene panels and prioritization schemes as part of routine autopsy evaluations to improve the identification of causative variants and potentially facilitate early diagnosis in first-degree relatives.

Clinical and genetic characterisation of a large Indian congenital myasthenic syndrome cohort.

Congenital myasthenic syndromes (CMS) are a rare group of inherited disorders caused by gene defects associated with the neuromuscular junction and potentially treatable with commonly available medications such as acetylcholinesterase inhibitors and ß2 adrenergic receptor agonists. In this study, we identified and genetically characterized the largest cohort of CMS patients from India to date. Genetic testing of clinically suspected patients evaluated in a South Indian hospital during the period 2014-19 was carried out by standard diagnostic gene panel testing or using a two-step method that included hotspot screening followed by whole-exome sequencing. In total, 156 genetically diagnosed patients (141 families) were characterized and the mutational spectrum and genotype-phenotype correlation described. Overall, 87 males and 69 females were evaluated, with the age of onset ranging from congenital to fourth decade (mean 6.6 ± 9.8 years). The mean age at diagnosis was 19 ± 12.8 (1-56 years), with a mean diagnostic delay of 12.5 ± 9.9 (0-49 years). Disease-causing variants in 17 CMS-associated genes were identified in 132 families (93.6%), while in nine families (6.4%), variants in genes not associated with CMS were found. Overall, postsynaptic defects were most common (62.4%), followed by glycosylation defects (21.3%), synaptic basal lamina genes (4.3%) and presynaptic defects (2.8%). Other genes found to cause neuromuscular junction defects (DES, TEFM) in our cohort accounted for 2.8%. Among the individual CMS genes, the most commonly affected gene was CHRNE (39.4%), followed by DOK7 (14.4%), DPAGT1 (9.8%), GFPT1 (7.6%), MUSK (6.1%), GMPPB (5.3%) and COLQ (4.5%). We identified 22 recurrent variants in this study, out of which eight were found to be geographically specific to the Indian subcontinent. Apart from the known common CHRNE variants p.E443Kfs*64 (11.4%) and DOK7 p.A378Sfs*30 (9.3%), we identified seven novel recurrent variants specific to this cohort, including DPAGT1 p.T380I and DES c.1023+5G>A, for which founder haplotypes are suspected. This study highlights the geographic differences in the frequencies of various causative CMS genes and underlines the increasing significance of glycosylation genes (DPAGT1, GFPT1 and GMPPB) as a cause of neuromuscular junction defects. Myopathy and muscular dystrophy genes such as GMPPB and DES, presenting as gradually progressive limb girdle CMS, expand the phenotypic spectrum. The novel genes MACF1 and TEFM identified in this cohort add to the expanding list of genes with new mechanisms causing neuromuscular junction defects.

The HDAC inhibitor CI-994 acts as a molecular memory aid by facilitating synaptic and intracellular communication after learning.

Long-term memory formation relies on synaptic plasticity, neuronal activity-dependent gene transcription, and epigenetic modifications. Multiple studies have shown that HDAC inhibitor (HDACi) treatments can enhance individual aspects of these processes and thereby act as putative cognitive enhancers. However, their mode of action is not fully understood. In particular, it is unclear how systemic application of HDACis, which are devoid of substrate specificity, can target pathways that promote memory formation. In this study, we explore the electrophysiological, transcriptional, and epigenetic responses that are induced by CI-994, a class I HDACi, combined with contextual fear conditioning (CFC) in mice. We show that CI-994­mediated improvement of memory formation is accompanied by enhanced long-term potentiation in the hippocampus, a brain region recruited by CFC, but not in the striatum, a brain region not primarily implicated in fear learning. Furthermore, using a combination of bulk and single-cell RNA-sequencing, we find that, when paired with CFC, HDACi treatment engages synaptic plasticity-promoting gene expression more strongly in the hippocampus, specifically in the dentate gyrus (DG). Finally, using chromatin immunoprecipitation-sequencing (ChIP-seq) of DG neurons, we show that the combined action of HDACi application and conditioning is required to elicit enhancer histone acetylation in pathways that underlie improved memory performance. Together, these results indicate that systemic HDACi administration amplifies brain region-specific processes that are naturally induced by learning.

Mpox Viral Lineage Analysis and Technique Development Using Next-generation Sequencing Approach.

BACKGROUND: In response to Mpox endemic and public health emergency, DCHHS aimed to develop NGS based techniques to streamline Mpox viral clade and lineage analysis. METHODS: The Mpox sequencing workflow started with DNA extraction and adapted Illumina's COVIDSeq assay using hMpox primer pools from Yale School of Public Health. Sequencing steps included cDNA amplification, tagmentation, PCR indexing, pooling libraries, sequencing on MiSeq, data analysis, and report generation. The bioinformatic analysis comprised read assembly and consensus sequence mapping to reference genomes and variant identification, and utilized pipelines including Illumina BaseSpace, NextClade, CLC Workbench, Terra.bio for data quality control (QC) and validation. RESULTS: In total, 171 mpox samples were sequenced using modified COVIDSeq workflow and QC metrics were assessed for read quality, depth, and coverage. Multiple analysis pipelines identified the West African clade IIb as the only clade during peak Mpox infection from July through October 2022. Analyses also indicated lineage B.1.2 as the dominant variant comprising the majority of Mpox viral genomes (77.7%), implying its geographical distribution in the United States. Viral sequences were uploaded to GISAID EpiPox. CONCLUSIONS: We developed NGS workflows to precisely detect and analyze mpox viral clade and lineages aiding in public health genomic surveillance.

Sequence Data From a Travel-Associated Case of Microcephaly Highlight a Persisting Risk due to Zika Virus Circulation in Thailand.

Zika virus has been circulating in Thailand since 2002 through continuous but likely low-level circulation. Here, we describe an infection in a pregnant woman who traveled to Thailand and South America during her pregnancy. By combining phylogenetic analysis with the patient's travel history and her pregnancy timeline, we confirmed that she likely got infected in Thailand at the end of 2021. This imported case of microcephaly highlights that Zika virus circulation in the country still constitutes a health risk, even in a year of lower incidence. MAIN POINTS: Here we trace the origin of travel-acquired microcephaly to Thailand, providing additional evidence that pre-American lineages of Zika virus can harm the fetus and highlighting that Zika virus constitutes a health threat even in a year of lower incidence.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells.

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3ß TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3ß sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/ß sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

Exploring JC Polyomavirus Sequences and Human Gene Expression in Brain Tissue of Patients With Progressive Multifocal Leukoencephalopathy.

Progressive multifocal leukoencephalopathy (PML) is a rare neurological condition associated with reactivation of dormant JC polyomavirus (JCPyV). In this study, we characterized gene expression and JCPyV rearrangements in PML brain tissue. Infection of white matter astrocytes and oligodendrocytes as well as occasional brain cortex neurons was shown. PML brain harbored exclusively rearranged JCPyV variants. Viral transcripts covered the whole genome on both strands. Strong differential expression of human genes associated with neuroinflammation, blood-brain barrier permeability, and neurodegenerative diseases was shown. Pathway analysis revealed wide immune activation in PML brain. The study provides novel insights into the pathogenesis of PML.

Revealing the unseen: next generation sequencing for early detection of drug-resistant cytomegalovirus variants upon letermovir prophylaxis failure.

In allogeneic hematopoietic cell transplant (HCT)-recipients, prophylactic management strategies are essential for preventing CMV-reactivation and associated disease. We report on a 63-year-old male patient with a D-/R+ CMV-serostatus, who showed ongoing low-level CMV-replication post-HCT despite receiving letermovir prophylaxis. Sanger-sequencing failed to detect drug resistance mutations (DRM) until CMV-pneumonitis developed, revealing a UL56-C325R-DRM linked to high-level letermovir resistance. Retrospective analysis with next-generation-sequencing (NGS) revealed the DRM at a low frequency of 6% two weeks prior to detection by Sanger-sequencing. This study highlights the importance of advanced NGS-methods for early detection of CMV-DRMs, allowing for faster adjustments in antiviral treatment strategies.