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Norovirus is recognized as one of the leading causes of acute gastroenteritis outbreaks. Genotype GII.9 was first detected in Norfolk, VA, USA, in 1997. However, the complete genome sequence of this genotype has not yet been determined. In this study, a complete genome sequence of GII.9[P7] norovirus, SCD1878_GII.9[P7], from a patient was determined using high-throughput sequencing and rapid amplification of cDNA ends (RACE) technology. The complete genome sequence of SCD1878_GII.9[P7] is 7544 nucleotides (nt) in length with a 3’ poly(A) tail and contains three open reading frames. Sequence comparisons indicated that SCD1878_GII.9[P7] shares 92.1%-92.3% nucleotide sequence identity with GII.P7 (AB258331 and AB039777) and 96.7%-97.4% identity with GII.9 (AY038599 and DQ379715). The results suggested that SCD1878_GII.9[P7] is a member of P genotype GII.P7 and G genotype GII.9. This viral sequence fills a gap at the whole-genome level for the GII.9 genotype. Supplementary Information The online version contains supplementary material available at 10.1007/s00705-021-05257-x.

Background High-sensitivity severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations. Methods We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection of 0.5 pg/mL (10.6 fmol/L) nucleocapsid antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning–based automatic microbubble image classifier to accurately identify positives and negatives and quantified and tracked antigen dynamics in intensive care unit coronavirus disease 2019 (COVID-19) inpatients and immunocompromised COVID-19 patients. Results Compared to qualitative reverse transcription−polymerase chain reaction methods, the MSAA demonstrated a positive percentage agreement of 97% (95% CI 92%–99%) and a negative percentage agreement of 97% (95% CI 94%–100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with cycle threshold values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden. Conclusions The MSAA enables sensitive and specific detection of acute infections and quantification and tracking of antigen burden and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.

Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily alongside much of modern biology. In terms of outright numbers of novel viruses discovered, however, the last few years have been by far the most transformative for the field. Advances in methods for identifying viral sequences in genomic and metagenomic datasets, coupled to the exponential growth of environmental sequencing, have greatly expanded the catalog of known viruses and fueled the tremendous growth of viral sequence databases. Development and implementation of new standards, along with careful study of the newly discovered viruses, have transformed and will continue to transform our understanding of microbial evolution, ecology, and biogeochemical cycles, leading to new biotechnological innovations across many diverse fields, including environmental, agricultural, and biomedical sciences.

Marcadors cel·lulars; Virus de la immunodeficiència humana; Persistència viral Marcadores celulares; Virus de la inmunodeficiencia humana; Persistencia viral Cell markers; Human immunodeficiency virus; Viral persistence Purpose of review To discuss the role of CD4+ T cells with active Human immunodeficiency virus (HIV), meaning infected cells with transcriptional and/or translational viral activity during antiretroviral therapy (ART), focusing on new technologies for its detection, potential cell markers for its characterization, and evidences on the contribution of the active HIV reservoir to long-term viral persistence. Recent findings HIV-infected cells expressing viral ribonucleic acid are systematically detected in subjects on long-term ART. In recent years, powerful new tools have provided significant insights into the nature, quantification, and identification of cells with active HIV, including the identification of new cell markers, and the presence of viral activity in specific cell populations located in different cellular and anatomical compartments. Moreover, studies on viral sequence integrity have identified cell clones with intact viral genomes and active viral transcription that could potentially persist for years. Together, new investigations support the notion that the active reservoir could represent a relevant fraction of long-term infected cells, and therefore, the study of its cell sources and mechanisms of maintenance could represent a significant advance in our understanding of viral persistence and the development of new curative strategies. Summary The presence of HIV-infected cells with viral expression during ART has been traditionally overlooked for years. Based on recent investigations, this active viral reservoir could play an important role in HIV persistence. This work was supported by the Spanish Secretariat of Science and Innovation and FEDER funds (grants SAF2015-67334-R and RTI2018-101082-B-I00 [MINECO/FEDER]), and the Fundació La Marató TV3 (grant 201805-10FMTV3). M.J.B. is supported by the Miguel Servet program funded by the Spanish Health Institute Carlos III (CP17/00179). A.A.-G. is supported by the Spanish Secretariat of Science and Innovation Ph.D. fellowship (BES-2016-076382). The funders had no role in the decision to publish or the preparation of the manuscript.

BACKGROUND Understanding geographic patterns of HIV transmission is critical to designing effective interventions. We characterized geographic proximity by transmission risk and urban-rural characteristics among people with closely related HIV strains suggestive of potential transmission relationships. METHODS We analyzed US National HIV Surveillance System data of people diagnosed between 2010 and 2016 with a reported HIV-1 partial polymerase nucleotide sequence. We used HIV TRAnsmission Cluster Engine (HIV-TRACE) to identify sequences linked at a genetic distance of ≤0.5%. For each linked person, we assessed median distances between counties of residence at diagnosis by transmission category and urban-rural classification, weighting observations to account for persons with multiple linked sequences. RESULTS There were 24,743 persons with viral sequence linkages to at least one other person included in this analysis. Overall, half (50.9%) of persons with linked viral sequences resided in different counties, and the median distance from persons with linked viruses was 11 km/7 miles [interquartile range (IQR), 0-145 km/90 miles]. Median distances were highest for men who have sex with men (MSM: 14 km/9 miles; IQR, 0-179 km/111 miles) and MSM who inject drugs, and median distances increased with increasing rurality (large central metro: 0 km/miles; IQR, 0-83 km/52 miles; nonmetro: 103 km/64 miles; IQR, 40 km/25 miles-316 km/196 miles). CONCLUSION Transmission networks in the United States involving MSM, MSM who inject drugs, or persons living in small metro and nonmetro counties may be more geographically dispersed, highlighting the importance of coordinated health department efforts for comprehensive follow-up and linkage to care.

Background GenBank contains over 3 million viral sequences. The National Center for Biotechnology Information (NCBI) previously made available a tool for validating and annotating influenza virus sequences that is used to check submissions to GenBank. Before this project, there was no analogous tool in use for non-influenza viral sequence submissions. Results We developed a system called VADR (Viral Annotation DefineR) that validates and annotates viral sequences in GenBank submissions. The annotation system is based on the analysis of the input nucleotide sequence using models built from curated RefSeqs. Hidden Markov models are used to classify sequences by determining the RefSeq they are most similar to, and feature annotation from the RefSeq is mapped based on a nucleotide alignment of the full sequence to a covariance model. Predicted proteins encoded by the sequence are validated with nucleotide-to-protein alignments using BLAST. The system identifies 43 types of “alerts” that (unlike the previous BLAST-based system) provide deterministic and rigorous feedback to researchers who submit sequences with unexpected characteristics. VADR has been integrated into GenBank’s submission processing pipeline allowing for viral submissions passing all tests to be accepted and annotated automatically, without the need for any human (GenBank indexer) intervention. Unlike the previous submission-checking system, VADR is freely available (https://github.com/nawrockie/vadr) for local installation and use. VADR has been used for Norovirus submissions since May 2018 and for Dengue virus submissions since January 2019. Since March 2020, VADR has also been used to check SARS-CoV-2 sequence submissions. Other viruses with high numbers of submissions will be added incrementally. Conclusion VADR improves the speed with which non-flu virus submissions to GenBank can be checked and improves the content and quality of the GenBank annotations. The availability and portability of the software allow researchers to run the GenBank checks prior to submitting their viral sequences, and thereby gain confidence that their submissions will be accepted immediately without the need to correspond with GenBank staff. Reciprocally, the adoption of VADR frees GenBank staff to spend more time on services other than checking routine viral sequence submissions.

The use of computational techniques to analyze viral sequence data and ultimately inform public health intervention has become increasingly common in the realm of epidemiology. These methods typically attempt to make epidemiological inferences based on multiple sequence alignments and phylogenies estimated from the raw sequence data. Like all estimation techniques, multiple sequence alignment and phylogenetic inference tools are error-prone, and the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly-used workflows for conducting viral phylogenetic analyses on simulated viral sequence data modeling HIV, HCV, and Ebola, and we computed multiple methods of accuracy motivated by transmission clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was orders of magnitude faster than the other tools, and when the other tools were used to optimize branch lengths along a fixed topology provided by FastTree 2 (i.e., no tree search), the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime. Our results indicate that an ideal workflow for viral phylogenetic inference is to (1) use MAFFT to perform MSA, (2) use FastTree 2 under the GTR model with discrete gamma-distributed site-rate heterogeneity to quickly obtain a reasonable tree topology, and (3) use RAxML-NG to optimize branch lengths along the fixed FastTree 2 topology.

The current COVID-19 pandemic is a massive source of global disruption, having led so far to two hundred and fifty million COVID-19 cases and almost five million deaths worldwide. It was recognized in the beginning that only an effective vaccine could lead to a way out of the pandemic, and therefore the race for the COVID-19 vaccine started immediately, boosted by the availability of the viral sequence data. Two novel vaccine platforms, based on mRNA technology, were developed in 2020 by Pfizer-BioNTech and Moderna Therapeutics (comirnaty® and spikevax®, respectively), and were the first ones presenting efficacies higher than 90%. Both consisted of N1-methyl-pseudouridine-modified mRNA encoding the SARS-COVID-19 Spike protein and were delivered with a lipid nanoparticle (LNP) formulation. Because the delivery problem of ribonucleic acids had been known for decades, the success of LNPs was quickly hailed by many as the unsung hero of COVID-19 mRNA vaccines. However, the clinical trial efficacy results of the Curevac mRNA vaccine (CVnCoV) suggested that the delivery system was not the only key to the success. CVnCoV consisted of an unmodified mRNA (encoding the same spike protein as Moderna and Pfizer-BioNTech’s mRNA vaccines) and was formulated with the same LNP as Pfizer-BioNTech’s vaccine (Acuitas ALC-0315). However, its efficacy was only 48%. This striking difference in efficacy could be attributed to the presence of a critical RNA modification (N1-methyl-pseudouridine) in the Pfizer-BioNTech and Moderna’s mRNA vaccines (but not in CVnCoV). Here we highlight the features of N1-methyl-pseudouridine and its contributions to mRNA vaccines.

Viral sequence integrations in the human genome have been implicated in various human diseases. Viral integrations remain among the most challenging-to-detect structural changes of the human genome. No studies have systematically analyzed how molecular and bioinformatics factors affect the power (sensitivity) to detect viral integrations using high-throughput sequencing (HTS). We selected a wide-range of molecular and bioinformatics factors covering genome sequence characteristics, HTS features, and viral integration detection. We designed a fast simulation-based framework to model the process of detecting variable viral integration events in the human genome. We then examined the associations of selected factors with viral integration detection power. We identified six factors that significantly affected viral integration detection power ( P

Epidemiological models are commonly fit to case data to estimate model parameters and to infer unobserved disease dynamics. Epidemiological models have also been fit to viral sequence data using phylodynamic inference approaches that rely on the reconstruction of viral phylogenies. However, especially early on in an expanding viral population, phylogenetic uncertainty can be substantial and methods that require integration over this uncertainty can be computationally intensive. Moreover, these approaches require estimation of parameters associated with models of sequence evolution in addition to the estimation of epidemiological parameters that are of primary interest. Here, we present an alternative approach to phylodynamic inference that can be used early on during the expansion of a viral lineage that circumvents the need for phylogenetic tree reconstruction. Our “tree-free” approach instead relies on quantifying the number of segregating sites observed in sets of sequences over time and using this trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo (SMC) framework. Using forward simulations, we first show that epidemiological parameters and processes leave characteristic signatures in segregating site trajectories, demonstrating that these trajectories have the potential to be used for interfacing epidemiological models with sequence data. We then show that our proposed approach accurately recovers key epidemiological quantities such as the basic reproduction number and the timing of the index case from mock data simulated under a single-introduction scenario. Finally, we apply our approach to SARS-CoV-2 sequence data from France, estimating a reproductive number of approximately 2.5 to 2.7 under an epidemiological model structure that allows for multiple introductions, consistent with estimates from epidemiological surveillance data. Our findings indicate that “tree-free” statistical inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics early on in an epidemic or during the expansion of a viral lineage.

Significance Mechanisms of many human chronic diseases involve abnormal action of the immune system and/or altered metabolism. The microbiome, an important regulator of metabolic and immune-related phenotypes, has been shown to be associated with or participate in the development of a variety of chronic diseases. Viruses of bacteria (i.e., “phages”) are ubiquitous and mysterious, and several studies have shown that phages exert great control over the behavior—and misbehavior—of their host bacteria. This study uses techniques to discover and analyze over 45,000 viruses associated with human bodies. The abundance of over 2,000 specific phages is found to correlate with a variety of common chronic diseases., Despite remarkable strides in microbiome research, the viral component of the microbiome has generally presented a more challenging target than the bacteriome. This gap persists, even though many thousands of shotgun sequencing runs from human metagenomic samples exist in public databases, and all of them encompass large amounts of viral sequence data. The lack of a comprehensive database for human-associated viruses has historically stymied efforts to interrogate the impact of the virome on human health. This study probes thousands of datasets to uncover sequences from over 45,000 unique virus taxa, with historically high per-genome completeness. Large publicly available case-control studies are reanalyzed, and over 2,200 strong virus–disease associations are found.

This protocol shows how to use VirSorter2, checkV, DRAMv and some manual curation criteria for viral sequence identification.

Motivation Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Results Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ∼24% on average (up to ∼42%) and especially improved the recovery of often-missed shorter contigs ( Availability and implementation The resources are available on the iVirus platform at (doi.org/10.25739/9vze-4143). Supplementary information Supplementary data are available at Bioinformatics online.

Covid-19 has resulted in the death of more than 1,500,000 individuals. Due to the pandemic’s severity, thousands of genomes have been sequenced and publicly stored with extensive records, an unprecedented amount of data for an outbreak in a single year. Simultaneously, prediction models offered region-specific and often contradicting results, while states or countries implemented mitigation strategies with little information on success, precision, or agreement with neighboring regions. Even though viral transmissions have been already documented in a historical and geographical context, few studies aimed to model geographic and temporal flow from viral sequence information. Here, using a case study of 7 states, we model the flow of the Covid-19 outbreak with respect to phylogenetic information, viral migration, inter- and intra- regional connectivity, epidemiologic and demographic characteristics. By assessing regional connectivity from genomic variants, we can significantly improve predictions in modeling the viral spread and intensity.Contrary to previous results, our study shows that the vast majority of the first outbreak can be traced to very few lineages, despite the existence of multiple worldwide transmissions. Moreover, our results show that while the distance from hotspots is initially important, connectivity becomes increasingly significant as the virus establishes itself. Similarly, isolated local strategies-such as relying on herd immunity-can negatively impact neighboring states. Our work suggests that we can achieve more efficient unified mitigation strategies with selective interventions.

Sequencing of the Spike gene is definitively the gold standard to define variants because it allows the recognition of all mutations/deletions/insertions affecting the whole viral sequence. However, this method is technically laborious and time-consuming.

Human blood metagenomics has revealed the presence of different types of viruses in apparently healthy subjects. By far, anelloviruses constitute the viral family that is more frequently found in human blood, although amplification biases and contaminations pose a major challenge in this field. To investigate this further, we subjected pooled plasma samples from 120 healthy donors in Spain to high-speed centrifugation, RNA and DNA extraction, random amplification, and massive parallel sequencing. Our results confirm the extensive presence of anelloviruses in such samples, which represented nearly 97% of the total viral sequence reads obtained. We assembled 114 different viral genomes belonging to this family, revealing remarkable diversity. Phylogenetic analysis of ORF1 suggested 28 potentially novel anellovirus species, 24 of which were validated by Sanger sequencing to discard artifacts. These findings underscore the importance of implementing more efficient purification procedures that enrich the viral fraction as an essential step in virome studies and question the suggested pathological role of anelloviruses., This work was supported by Grant SAF2017-82287-R from the Spanish Ministry of Economy, Industry and competitiveness (MINECO) and FEDER funding. JMC was supported by a Ramón y Cajal postdoctoral contract from MINECO.

Great efforts are devoted to end the HIV epidemic as it continues to have profound public health consequences in the United States and throughout the world, and new interventions and strategies are continuously needed. The use of HIV sequence data to infer transmission networks holds much promise to direct public heath interventions where they are most needed. As these new methods are being implemented, evaluating their benefits is essential. In this paper, we recognize challenges associated with such evaluation, and make the case that overcoming these challenges is key to the use of HIV sequence data in routine public health actions to disrupt HIV transmission networks.

Evolution is a process of change where mutations in the viral RNA are selected based on their fitness for replication and survival. Given that current phylogenetic analysis of SARS-CoV-2 identifies new viral clades after they exhibit evolutionary selections, one wonders whether we can identify the viral selection and predict the emergence of new viral clades? Inspired by the Kolmogorov complexity concept, we propose a generative complexity (algorithmic) framework capable to analyze the viral RNA sequences by mapping the multiscale nucleotide dependencies onto a state machine, where states represent subsequences of nucleotides and state-transition probabilities encode the higher order interactions between these states. We apply computational learning and classification techniques to identify the active state-transitions and use those as features in clade classifiers to decipher the transient mutations (still evolving within a clade) and stable mutations (typical to a clade). As opposed to current analysis tools that rely on the edit distance between sequences and require sequence alignment, our method is computationally local, does not require sequence alignment and is robust to random errors (substitution, insertions and deletions). Relying on the GISAID viral sequence database, we demonstrate that our method can predict clade emergence, potentially aiding with the design of medications and vaccines.

Motivation High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings. Results The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals. Availability and implementation We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file. Supplementary information Supplementary data are available at Bioinformatics online.

Background Respiratory syncytial virus (RSV) is a major cause of childhood medically attended respiratory infection (MARI). Methods We conducted a randomized, double-blind, placebo-controlled phase 3 trial in 1154 preterm infants of 1 or 2 doses of suptavumab, a human monoclonal antibody that can bind and block a conserved epitope on RSV A and B subtypes, for the prevention of RSV MARI. The primary endpoint was proportion of subjects with RSV-confirmed hospitalizations or outpatient lower respiratory tract infection (LRTI). Results There were no significant differences between primary endpoint rates (8.1%, placebo; 7.7%, 1-dose; 9.3%, 2-dose). Suptavumab prevented RSV A infections (relative risks, .38; 95% confidence interval [CI], .14–1.05 in the 1-dose group and .39 [95% CI, .14–1.07] in the 2-dose group; nominal significance of combined suptavumab group vs placebo; P = .0499), while increasing the rate of RSV B infections (relative risk 1.36 [95% CI, .73–2.56] in the 1-dose group and 1.69 [95% CI, .92–3.08] in the 2-dose group; nominal significance of combined suptavumab group vs placebo; P = .12). Sequenced RSV isolates demonstrated no suptavumab epitope changes in RSV A isolates, while all RSV B isolates had 2–amino acid substitution in the suptavumab epitope that led to loss of neutralization activity. Treatment emergent adverse events were balanced across treatment groups. Conclusions Suptavumab did not reduce overall RSV hospitalizations or outpatient LRTI because of a newly circulating mutant strain of RSV B. Genetic variation in circulating RSV strains will continue to challenge prevention efforts. Clinical Trials Registration NCT02325791., In a randomized, double-blind, placebo-controlled phase 3 trial in 1154 preterm infants, 1 or 2 doses of suptavumab did not reduce overall respiratory syncytial virus (RSV) hospitalizations or outpatient LRTI because of a newly circulating mutant strain of RSV-B.

MotivationThe Coronavirus Disease 2019 (COVID-19) pandemic poses a huge threat to human public health. Viral sequence data plays an important role in the scientific prevention and control of epidemics. A comprehensive virus database will be vital useful for virus data retrieval and deep analysis. To promote sharing of virus data, several virus databases and related analyzing tools have been created.ResultsTo facilitate virus research and promote the global sharing of virus data, we present here VirusDIP, a one-stop service platform for archive, integration, access, analysis of virus data. It accepts the submission of viral sequence data from all over the world and currently integrates data resources from the National GeneBank Database (CNGBdb), Global initiative on sharing all influenza data (GISAID), and National Center for Biotechnology Information (NCBI). Moreover, based on the comprehensive data resources, BLAST sequence alignment tool and multi-party security computing tools are deployed for multi-sequence alignment, phylogenetic tree building and global trusted sharing. VirusDIP is gradually establishing cooperation with more databases, and paving the way for the analysis of virus origin and evolution. All public data in VirusDIP are freely available for all researchers worldwide.Availabilityhttps://db.cngb.org/virus/Contactweixiaofeng@cngb.org

Highly pathogenic avian influenza A(H5N8) viruses have been detected in several continents. However, limited viral sequence data are available from countries in the Middle East. We report full-genome analyses of highly pathogenic H5N8 viruses recently detected in different provinces in Saudi Arabia.

Viral reads identification is one of the important steps in metagenomic data analysis. It shows up the diversity of the microbial communities and the functional characteristics of microorganisms. There are various tools that can identify viral reads in mixed metagenomic data using similarity and statistical tools. However, the lack of available genome diversity is a serious limitation to the existing techniques. In this work, we applied natural language processing approaches for document classification in analyzing metagenomic sequences. Text featurization is presented by treating DNA similar to natural language. These techniques reveal the importance of using the text feature extraction pipeline in sequence identification by transforming DNA base pairs into a set of characters with a term frequency and inverse document frequency techniques. Various machine learning classification algorithms are applied to viral identification tasks such as logistic regression and multi-layer perceptron. Moreover, we compared classical machine learning algorithms with VirFinder and VirNet, our deep attention model for viral reads identification on generated fragments of viruses and bacteria for benchmarking viral reads identification tools. Then, as a verification of our tool, It was applied to a simulated microbiome and virome data for tool verification and real metagenomic data of Roche 454 and Illumina for a case study.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes Coronavirus Disease 2019 (COVID-19), has caused a global pandemic. Antibodies are powerful biotherapeutics to fight viral infections; however, discovery of the most potent and broadly acting clones can be lengthy. Here, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers. Using this platform, half-maximal inhibitory concentration (IC50) values as low as 9 × 10−14 M were achieved as a result of up to 10,000-fold potency enhancements. Combination of three different antibody specificities and the fragment crystallizable (Fc) domain on a single multivalent molecule conferred the ability to overcome viral sequence variability together with outstanding potency and Ig-like in vivo bioavailability. This MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.

Sewage samples have a high potential benefit for surveillance of circulating pathogens because they are easy to obtain and reflect population-wide circulation of pathogens. These type of samples typically contain a great diversity of viruses. Therefore, one of the main challenges of metagenomic sequencing of sewage for surveillance is sequence annotation and interpretation. Especially for high-threat viruses, false positive signals can trigger unnecessary alerts, but true positives should not be missed. Annotation thus requires high sensitivity and specificity. To better interpret annotated reads for high-threat viruses, we attempt to determine how classifiable they are in a background of reads of closely related low-threat viruses. As an example, we attempted to distinguish poliovirus reads, a virus of high public health importance, from other enterovirus reads. A sequence-based deep learning algorithm was used to classify reads as either polio or non-polio enterovirus. Short reads were generated from 500 polio and 2,000 non-polio enterovirus genomes as a training set. By training the algorithm on this dataset we try to determine, on a single read level, which short reads can reliably be labeled as poliovirus and which cannot. After training the deep learning algorithm on the generated reads we were able to calculate the probability with which a read can be assigned to a poliovirus genome or a non-poliovirus genome. We show that the algorithm succeeds in classifying the reads with high accuracy. The probability of assigning the read to the correct class was related to the location in the genome to which the read mapped, which conformed with our expectations since some regions of the genome are more conserved than others. Classifying short reads of high-threat viral pathogens seems to be a promising application of sequence-based deep learning algorithms. Also, recent developments in software and hardware have facilitated the development and training of deep learning algorithms. Further plans of this work are to characterize the hard-to-classify regions of the poliovirus genome, build larger training databases, and expand on the current approach to other viruses.

Marseilleviridae is a family of viruses which have only been propagated in acanthamoeba. Marseillevirus sequences have been recently detected in different human matrices by viral metagenomics. Single-center studies worldwide have estimated a low prevalence of marseillevirus both in symptomatic patients and in healthy donors but, to date, no informations are available on the prevalence of this giant virus in Italy. By a polymerase chain reaction targeting the ORF152 viral sequence, we tested sera from 197 immunosuppressed patients and 285 healthy donors, and 63 and 30 respiratory and cerebrospinal fluid samples, respectively, of patients with various clinical conditions and referring the Virology Division for diagnostic purposes. We observed no evidence of Marseillevirus DNA in all 575 samples tested. Marseillevirus probably does not cause infection in human.

Microbes drive critical ecosystem functions and affect global nutrient cycling along with human health and disease. They do so under strong constraints exerted by viruses, which shape microbial communities’ structure and shift host cell metabolism during infection., Microbes drive critical ecosystem functions and affect global nutrient cycling along with human health and disease. They do so under strong constraints exerted by viruses, which shape microbial communities’ structure and shift host cell metabolism during infection. While the majority of viruses and their associated impacts remain poorly characterized, a number of mechanisms by which viruses alter microbial cells and ecosystems have already been revealed. Here I outline how a comprehensive host-resolved mapping of viral sequence space will enable a thorough characterization of virus-encoded mechanisms for microbial manipulation. With soon-to-be millions of virus genomes obtained from metagenomes, one of the major challenges resides in the development of methods for high-throughput and high-resolution virus-host pairing, before multi-omics approaches can be leveraged to fully decipher virus-host dynamics in nature. Beyond novel fundamental biological knowledge, these studies will likely provide new molecular tools enabling a precise engineering of microbial cells and communities.

Summary: Analysis of HIV-1 virions from participants infected in a randomized controlled preventive HIV-1 vaccine efficacy trial can help elucidate mechanisms of partial protection. By comparing the genetic sequence of viruses from vaccine and placebo recipients to the sequence of the vaccine itself, a technique called ‘sieve analysis’, one can identify functional specificities of vaccine-induced immune responses. We have created an interactive web-based visualization and data access tool for exploring the results of sieve analyses performed on four major preventive HIV-1 vaccine efficacy trials: (i) the HIV Vaccine Trial Network (HVTN) 502/Step trial, (ii) the RV144/Thai trial, (iii) the HVTN 503/Phambili trial and (iv) the HVTN 505 trial. The tool acts simultaneously as a platform for rapid reinterpretation of sieve effects and as a portal for organizing and sharing the viral sequence data. Access to these valuable datasets also enables the development of novel methodology for future sieve analyses. Availability and Implementation: Visualization: http://sieve.fredhutch.org/viz. Source code: https://github.com/nkullman/SIEVE. Data API: http://sieve.fredhutch.org/data. Contact: agartlan@fredhutch.org

Objectives Sample preparation for high-throughput sequencing (HTS) includes treatment with various laboratory components, potentially carrying viral nucleic acids, the extent of which has not been thoroughly investigated. Our aim was to systematically examine a diverse repertoire of laboratory components used to prepare samples for HTS in order to identify contaminating viral sequences. Methods A total of 322 samples of mainly human origin were analysed using eight protocols, applying a wide variety of laboratory components. Several samples (60% of human specimens) were processed using different protocols. In total, 712 sequencing libraries were investigated for viral sequence contamination. Results Among sequences showing similarity to viruses, 493 were significantly associated with the use of laboratory components. Each of these viral sequences had sporadic appearance, only being identified in a subset of the samples treated with the linked laboratory component, and some were not identified in the non-template control samples. Remarkably, more than 65% of all viral sequences identified were within viral clusters linked to the use of laboratory components. Conclusions We show that high prevalence of contaminating viral sequences can be expected in HTS-based virome data and provide an extensive list of novel contaminating viral sequences that can be used for evaluation of viral findings in future virome and metagenome studies. Moreover, we show that detection can be problematic due to stochastic appearance and limited non-template controls. Although the exact origin of these viral sequences requires further research, our results support laboratory-component-linked viral sequence contamination of both biological and synthetic origin.

Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.

The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/.

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases., Author summary In order to spread, pathogens must not only be able to grow within an infected host, but also transmit to found new infections. Population genetics can exploit genome sequence data to provide a great deal of insight into transmission processes. For example, the number of particles which found a new infection determine the extent to which genetic diversity is passed from host to host. The identification of genetic variants which increase the propensity of a pathogen to transmit from host to host is a valuable step in understanding how an infection might spread. Here we set out a new population genetic framework for understanding transmission events from genome sequence data collected before and after transmission. Our approach corrects for the shortcomings of existing methods for this purpose, setting out a new baseline for the statistical analysis of transmission events. We demonstrate the ability of our method to draw novel quantitative insights by application to data from simulated and real transmission events.

Molecular phylogenetics is often used to estimate the time since the divergence of modern gene sequences. For highly diverged sequences, such phylogenetic techniques sometimes estimate surprisingly recent divergence times. In the case of viruses, independent evidence indicates that the estimates of deep divergence times from molecular phylogenetics are sometimes too recent. This discrepancy is caused in part by inadequate models of purifying selection leading to branch-length underestimation. Here we examine the effect on branch-length estimation of using models that incorporate experimental measurements of purifying selection. We find that models informed by experimentally measured site-specific amino-acid preferences estimate longer deep branches on phylogenies of influenza virus hemagglutinin. This lengthening of branches is due to more realistic stationary states of the models, and is mostly independent of the branch-length extension from modeling site-to-site variation in amino-acid substitution rate. The branch-length extension from experimentally informed site-specific models is similar to that achieved by other approaches that allow the stationary state to vary across sites. However, the improvements from all of these site-specific but time homogeneous and site independent models are limited by the fact that a protein’s amino-acid preferences gradually shift as it evolves. Overall, our work underscores the importance of modeling site-specific amino-acid preferences when estimating deep divergence times—but also shows the inherent limitations of approaches that fail to account for how these preferences shift over time.

Intrahost viral sequence diversity can be evaluated over multiple genomic regions using next-generation sequencing (NGS) and scaled to population-level diversity to identify recent human immunodeficiency virus type 1 infection. Using Primer-ID NGS, we sequenced the reverse transcriptase (RT) and env V1-V3 regions from persons with known infection dates, and assessed the mean (π) and first quintile of pairwise diversity distributions over time. The receiver operating characteristic area under the curve (AUC) of RT and V1-V3 combined showed excellent discrimination of recent infection

Between January and May in 2018, 17 male cases of hepatitis A were reported in Yokohama, Japan. Of these, 14 identified as men who have sex with men. The viral sequence in this outbreak was same as that of the recent European and Taiwanese outbreaks strain.

1 p. Correcciones a la Fig. 7 del artículo "Potato Virus Y HCPro Suppression of Antiviral Silencing in Nicotiana benthamiana Plants Correlates with Its Ability To Bind In Vivo to 21- and 22-Nucleotide Small RNAs of Viral Sequence"

HIV-1 sequence diversity presents a major challenge for the clinical development of broadly neutralizing antibodies (bNAbs) for both therapy and prevention. Sequence variation in critical bNAb epitopes has been observed in the majority of HIV-1 infected individuals and can lead to viral escape following bNAb monotherapy in humans. In this study, we show that viral sequence diversity can limit both the therapeutic and prophylactic efficacy of bNAbs in rhesus monkeys. We first demonstrate that monotherapy with the V3 glycan-dependent antibody 10-1074, but not PGT121, results in rapid selection of pre-existing viral variants containing N332/S334 escape mutations and loss of therapeutic efficacy in SHIV-SF162P3-infected rhesus monkeys. We then show that the V3 glycan-dependent antibody PGT121 alone and the V2 glycan-dependent antibody PGDM1400 alone both fail to protect against a mixed challenge with SHIV-SF162P3 and SHIV-325C. In contrast, the combination of both bNAbs provides 100% protection against this mixed SHIV challenge. These data reveal that single bNAbs efficiently select resistant viruses from a diverse challenge swarm to establish infection, demonstrating the importance of bNAb cocktails for HIV-1 prevention.

An informal consortium of scientists from the biopharmaceutical industry, academia, and government agencies, including regulators, coalesced during 2013 to further explore advanced virus detection technologies or appropriate applications for characterization and evaluation of biologicals. As a Parenteral Drug Association task force, the Users Group came to focus on four key work areas that required better understanding and data generation: (1) evaluation of sample preparation and processing steps for different sample types, (2) determination of method sensitivity by performing spike recovery studies using selected virus stocks, (3) development of a reliable and comprehensive viral sequence database, and (4) evaluation of bioinformatic analysis pipelines, with primary focus on next-generation DNA sequencing platforms. The year of monthly working meetings and additional subgroup discussions culminated in the 2013 PDA/FDA Advanced Technologies for Virus Detection in the Evaluation of Biologicals Conference held November 13-14, 2013 in Bethesda, MD, which provided a forum for group participants and others for data sharing and knowledge exchange. The Users Group has continued its efforts during 2014 as a PDA Interest Group with increased participation from technology developers and contract research organizations.

For public health safety, vaccines and other pharmaceutical products as well as the raw materials used in their manufacture need to be tested for adventitious virus contamination. The current standard of practice is to develop culture-based or polymerase chain reaction assays for the types of viruses one might expect based upon the source of reagents used. High-throughput sequencing technology is well-suited for building an unbiased strategy for the purpose of adventitious virus detection. We have developed an approach to automate curation of publically available nucleotide sequences, and have practically balanced the desire to capture all viral diversity while simultaneously reducing the use of partial viral sequences that represent the largest source of false positive results. In addition, we describe an effective workflow for virus detection that can process sequence data from all currently available High-throughput sequencing technologies and produce a report that summarizes the weight of sequence data in support of each detected virus.

Motivation: Not only sequence data continue to outpace annotation information, but also the problem is further exacerbated when organisms are underrepresented in the annotation databases. This is the case with non-human-pathogenic viruses which occur frequently in metagenomic projects. Thus, there is a need for tools capable of detecting and classifying viral sequences. Results: We describe VIRALpro a new effective tool for identifying capsid and tail protein sequences, which are the cornerstones toward viral sequence annotation and viral genome classification. Availability and implementation: The data, software and corresponding web server are available from http://scratch.proteomics.ics.uci.edu as part of the SCRATCH suite. Contact: clovis.galiez@inria.fr or pfbaldi@uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.