PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network.

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.

vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

Early Introduction and Community Transmission of SARS-CoV-2 Omicron Variant, New York, New York, USA.

The Omicron variant of SARS-CoV-2 has become dominant in most countries and has raised significant global health concerns. As a global commerce center, New York, New York, USA, constantly faces the risk for multiple variant introductions of SARS-CoV-2. To elucidate the introduction and transmission of the Omicron variant in the city of New York, we created a comprehensive genomic and epidemiologic analysis of 392 Omicron virus specimens collected during November 25-December 11, 2021. We found evidence of 4 independent introductions of Omicron subclades, including the Omicron subclade BA.1.1 with defining substitution of R346K in the spike protein. The continuous genetic divergence within each Omicron subclade revealed their local community transmission and co-circulation in New York, including both household and workplace transmissions supported by epidemiologic evidence. Our study highlights the urgent need for enhanced genomic surveillance and effective response planning for better prevention and management of emerging SARS-CoV-2 variants.

Progress and challenge for computational quantification of tissue immune cells.

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2.

The life-threatening coronaviruses MERS-CoV, SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) have caused and will continue to cause enormous morbidity and mortality to humans. Virus-encoded noncoding RNAs are poorly understood in coronaviruses. Data mining of viral-infection-related RNA-sequencing data has resulted in the identification of 28 754, 720 and 3437 circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2, respectively. MERS-CoV exhibits much more prominent ability to encode circRNAs in all genomic regions than those of SARS-CoV-1/2. Viral circRNAs typically exhibit low expression levels. Moreover, majority of the viral circRNAs exhibit expressions only in the late stage of viral infection. Analysis of the competitive interactions of viral circRNAs, human miRNAs and mRNAs in MERS-CoV infections reveals that viral circRNAs up-regulated genes related to mRNA splicing and processing in the early stage of viral infection, and regulated genes involved in diverse functions including cancer, metabolism, autophagy, viral infection in the late stage of viral infection. Similar analysis in SARS-CoV-2 infections reveals that its viral circRNAs down-regulated genes associated with metabolic processes of cholesterol, alcohol, fatty acid and up-regulated genes associated with cellular responses to oxidative stress in the late stage of viral infection. A few genes regulated by viral circRNAs from both MERS-CoV and SARS-CoV-2 were enriched in several biological processes such as response to reactive oxygen and centrosome localization. This study provides the first glimpse into viral circRNAs in three deadly coronaviruses and would serve as a valuable resource for further studies of circRNAs in coronaviruses.

Compositional diversity and evolutionary pattern of coronavirus accessory proteins.

Accessory proteins play important roles in the interaction between coronaviruses and their hosts. Accordingly, a comprehensive study of the compositional diversity and evolutionary patterns of accessory proteins is critical to understanding the host adaptation and epidemic variation of coronaviruses. Here, we developed a standardized genome annotation tool for coronavirus (CoroAnnoter) by combining open reading frame prediction, transcription regulatory sequence recognition and homologous alignment. Using CoroAnnoter, we annotated 39 representative coronavirus strains to form a compositional profile for all of the accessary proteins. Large variations were observed in the number of accessory proteins of 1-10 for different coronaviruses, with SARS-CoV-2 and SARS-CoV having the most (9 and 10, respectively). The variation between SARS-CoV and SARS-CoV-2 accessory proteins could be traced back to related coronaviruses in other hosts. The genomic distribution of accessory proteins had significant intra-genus conservation and inter-genus diversity and could be grouped into 1, 4, 2 and 1 types for alpha-, beta-, gamma-, and delta-coronaviruses, respectively. Evolutionary analysis suggested that accessory proteins are more conservative locating before the N-terminal of proteins E and M (E-M), while they are more diverse after these proteins. Furthermore, comparison of virus-host interaction networks of SARS-CoV-2 and SARS-CoV accessory proteins showed that they share multiple antiviral signaling pathways, those involved in the apoptotic process, viral life cycle and response to oxidative stress. In summary, our study provides a tool for coronavirus genome annotation and builds a comprehensive profile for coronavirus accessory proteins covering their composition, classification, evolutionary pattern and host interaction.

Comparative viromes of Culicoides and mosquitoes reveal their consistency and diversity in viral profiles.

The genus Culicoides includes biting midges, some of which are vectors for viruses that cause diseases in humans and animals. Knowledge of the roles of Culicoides in viral ecology is inadequate. We collected ~300 000 samples of Culicoides and mosquitoes in 15 representative regions within Yunnan, China. Using mosquitoes as reference vectors, we designed a comparative virome strategy to study the viral composition, diversity, hosts and spatiotemporal distribution of Culicoides. A map of viromes in Culicoides and mosquitoes in Yunan province, China, was constructed. At the same locations, Culicoides and mosquitoes usually share a similar viral diversity. At least 10 important pathogenic viruses were detected from Culicoides. Many novel viruses were discovered, including 21 segmented viruses of Flaviviridae, 180 viruses of Monjiviricetes and 130 viruses of Bunyavirales. The findings demonstrate that Culicoides is an important part of viral ecology and should be studied and monitored for potentially emerging viruses.

An atlas of human viruses provides new insights into diversity and tissue tropism of human viruses.

MOTIVATION: Viruses continue to threaten human health. Yet, the complete viral species carried by humans and their infection characteristics have not been fully revealed. RESULTS: This study curated an atlas of human viruses from public databases and literature, and built the Human Virus Database (HVD). The HVD contains 1131 virus species of 54 viral families which were more than twice the number of the human-infecting virus species reported in previous studies. These viruses were identified in human samples including 68 human tissues, the excreta and body fluid. The viral diversity in humans was age-dependent with a peak in the infant and a valley in the teenager. The tissue tropism of viruses was found to be associated with several factors including the viral group (DNA, RNA or reverse-transcribing viruses), enveloped or not, viral genome length and GC content, viral receptors and the virus-interacting proteins. Finally, the tissue tropism of DNA viruses was predicted using a random-forest algorithm with a middle performance. Overall, the study not only provides a valuable resource for further studies of human viruses but also deepens our understanding toward the diversity and tissue tropism of human viruses. AVAILABILITY AND IMPLEMENTATION: The HVD is available at http://computationalbiology.cn/humanVirusBase/#/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic annotation and molecular evolution of monkeypox virus outbreak in 2022.

Monkeypox virus (MPXV) has generally circulated in West and Central Africa since its emergence. Recently, sporadic MPXV infections in several nonendemic countries have attracted widespread attention. Here, we conducted a systematic analysis of the recent outbreak of MPXV-2022, including its genomic annotation and molecular evolution. The phylogenetic analysis indicated that the MPXV-2022 strains belong to the same lineage of the MPXV strain isolated in 2018. However, compared with the MPXV strain in 2018, in total 46 new consensus mutations were observed in the MPXV-2022 strains, including 24 nonsynonymous mutations. By assigning mutations to 187 proteins encoded by the MPXV genome, we found that 10 proteins in the MPXV are more prone to mutation, including D2L-like, OPG023, OPG047, OPG071, OPG105, OPG109, A27L-like, OPG153, OPG188, and OPG210 proteins. In the MPXV-2022 strains, four and three nucleotide substitutions are observed in OPG105 and OPG210, respectively. Overall, our studies illustrated the genome evolution of the ongoing MPXV outbreak and pointed out novel mutations as a reference for further studies.

Changes in endemic patterns of respiratory syncytial virus infection in pediatric patients under the pressure of nonpharmaceutical interventions for COVID-19 in Beijing, China.

A series of nonpharmaceutical interventions (NPIs) was launched in Beijing, China, on January 24, 2020, to control coronavirus disease 2019. To reveal the roles of NPIs on the respiratory syncytial virus (RSV), respiratory specimens collected from children with acute respiratory tract infection between July 2017 and Dec 2021 in Beijing were screened by capillary electrophoresis-based multiplex PCR (CEMP) assay. Specimens positive for RSV were subjected to a polymerase chain reaction (PCR) and genotyped by G gene sequencing and phylogenetic analysis using iqtree v1.6.12. The parallel and fixed (paraFix) mutations were analyzed with the R package sitePath. Clinical data were compared using SPSS 22.0 software. Before NPIs launched, each RSV endemic season started from October/November to February/March of the next year in Beijing. After that, the RSV positive rate abruptly dropped from 31.93% in January to 4.39% in February 2020; then, a dormant state with RSV positive rates ≤1% from March to September, a nearly dormant state in October (2.85%) and November (2.98%) and a delayed endemic season in 2020, and abnormal RSV positive rates remaining at approximately 10% in summer until September 2021 were detected. Finally, an endemic RSV season returned in October 2021. There was a game between Subtypes A and B, and RSV-A replaced RSV-B in July 2021 to become the dominant subtype. Six RSV-A and eight RSV-B paraFix mutations were identified on G. The percentage of severe pneumonia patients decreased to 40.51% after NPIs launched. NPIs launched in Beijing seriously interfered with the endemic season of RSV.

How Eutrophication Promotes Exotic Aquatic Plant Invasion in the Lake Littoral Zone?

Eutrophication and exotic species invasion are key drivers of the global loss of biodiversity and ecosystem functions in lakes. We selected two exotic plants (Alternanthera philoxeroides and Myriophyllum aquaticum) and two native plants (Myriophyllum spicatum and Vallisneria spinulosa) to elucidate the effect of eutrophication on exotic plant invasiveness. We found that (1) elevated nutrient favored invasion of exotic species and inhibited growth of native plants. Species combinations and plant densities of native plants had limited effects on the resistance to invasion of the exotics. (2) A. philoxeroides featured the tightest connectivity among traits, which is consistent with its high competitive ability. Although eutrophication caused physiological stress to A. philoxeroides, it could effectively regulate enzyme activity and alleviate the stress. (3) M. aquaticum possessed strong tolerance to habitat disturbance and was highly disruptive to the surrounding plants. Eutrophication will exacerbate the adverse effects of M. aquaticum on the littoral ecosystem. (4) Nutrient enrichment reduced the biomass and relative growth rates of V. spinulosa and lowered phenolics and starch contents of M. spicatum, thereby making them more susceptible to habitat fluctuations. Overall, our study highlights how eutrophication alters the invasiveness of exotic plants and the resistance of native plants in the littoral zone, which is of relevance in a world with intensified human activities.

Mo-Ni-based Heterojunction with Fine-customized d-Band Centers for Hydrogen Production Coupled with Benzylamine Electrooxidation in Low Alkaline Medium.

Benzylamine electrooxidation reaction (BAOR) is a promising route to produce value-added, easy-separated benzonitrile, and effectively hoist H2 production. However, achieving excellent performance in low alkaline medium is a huge challenge. The performance is intimately correlated with effective coupling of HER and BAOR, which can be achieved by manipulating the d-electron structure of catalyst to regulate the active species from water. Herein, we constructed a biphasic Mo0.8 Ni0.2 N-Ni3 N heterojunction for enhanced bifunctional performance toward HER coupled with BAOR by customizing the d-band centers. Experimental and theoretical calculations indicate that charge transfer in the heterojunction causes the upshift of the d-band centers, which one side facilitates to decrease water activation energy and optimize H* adsorption on Mo0.8 Ni0.2 N for promoting HER activity, the other side favors to more easily produce and adsorb OH* from water for forming NiOOH on Ni3 N and optimizing adsorption energy of benzylamine, thus catalyzing BAOR effectively. Accordingly, it shows an industrial current density of 220 mA cm-2 at 1.59 V and high Faradaic efficiencies (>99 %) for H2 production and converting benzylamine to benzonitrile in 0.1 M KOH/0.5 M Na2 SO4 . This work guides the design of excellent bifunctional electrocatalysts for the scalable production of green hydrogen and value-added products.

Quantifying the spatiotemporal evolution characteristics of medical waste generation during the outbreak of public health emergencies.

Based on the medical waste quantity and patient data during the corona virus disease 2019 (COVID-19) outbreak in China, this study used scenario analysis to quantitatively analyze the temporal and spatial evolution of medical waste generation during the pandemics. First, the results show that the estimated medical waste per capita reached 15.4 kg/day if only patients were considered in Scenario 1, while the figures were reduced to 3.2 kg/day in Scenario 2 and 2.5 kg/day in Scenario 3 when the effects of both the patient type and the number of medical staffs were considered. The estimated results also demonstrated that the per capita medical waste related to the epidemic showed the characteristics of a U-shaped and trailing phenomenon over time. Then, the amount of medical waste related to the COVID-19 generated that generated due to COVID-19 was estimated in Hubei, Heilongjiang, Zhejiang, Henan and Hunan provinces under Scenario 2 and Scenario 3. The results indicated that the spatiotemporal evolution characteristics of five provinces show the significant differences, and the patient type has a remarkable influence on the generation of medical waste. Finally, a novel decomposition-ensemble approach was designed to make a better short-term forecasting effect for future medical waste generation in different provinces. Supplementary Information: The online version contains supplementary material available at 10.1007/s10163-022-01523-5.

sitePath: a visual tool to identify polymorphism clades and help find fixed and parallel mutations.

BACKGROUND: Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed. RESULTS: Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software. CONCLUSIONS: The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.

FluReassort: a database for the study of genomic reassortments among influenza viruses.

Genomic reassortment is an important genetic event in the generation of emerging influenza viruses, which can cause numerous serious flu endemics and epidemics within hosts or even across different hosts. However, there is no dedicated and comprehensive repository for reassortment events among influenza viruses. Here, we present FluReassort, a database for understanding the genomic reassortment events in influenza viruses. Through manual curation of thousands of literature references, the database compiles 204 reassortment events among 56 subtypes of influenza A viruses isolated in 37 different countries. FluReassort provides an interface for the visualization and evolutionary analysis of reassortment events, allowing users to view the events through the phylogenetic analysis with varying parameters. The reassortment networks in FluReassort graphically summarize the correlation and causality between different subtypes of the influenza virus and facilitate the description and interpretation of the reassortment preference among subtypes. We believe FluReassort is a convenient and powerful platform for understanding the evolution of emerging influenza viruses. FluReassort is freely available at https://www.jianglab.tech/FluReassort.

Potential intervariant and intravariant recombination of Delta and Omicron variants.

Among numerous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concerns, Omicron is more infectious and immune-escaping, while Delta is more pathogenic. Here, we provide evidence for both intervariant and intravariant recombination of the rapidly evolving new SARS-CoV-2 genomes, including XD/XE/XF and BA.3, raising concerns of potential more infectious, immune-escaping, and disease-causing Omicron and Delta-Omicron variants.

Detection and typing of dengue virus by one-step RT-PCR-based high-resolution melting assay.

Dengue is a rapidly spreading arboviral disease that can be transmitted through any of the four types of dengue virus (DENV) serotypes. Previous studies have observed that individuals who have a pre-existing secondary infection due to a different dengue serotype, experience severe forms of this disease. During a DENV outbreak, a time-sensitive preliminary diagnosis of the origin of DENV might be useful in controlling the epidemic. Here, we developed a rapid and accurate one-step TB Green RT-PCR-based high-resolution melting (HRM) assay to identify and serotype DENV using serotyping primers based on the alignment with the E gene. This assay had a detection limit of 7.7 × 102 (DENV 1), 3.8 × 102 (DENV 2), 6.2 × 102 (DENV 3), and 1.2 × 103 (DENV 4) RNA copies/mL. No cross-reactivity with the Chikungunya, Zika, and Japanese encephalitis viruses was observed. The feasibility of using this assay for clinical diagnosis was evaluated in DENV-positive patient sera. The HRM assay and the RT-qPCR had complete matched results derived from DENV detection, including 51 serum positive and 20 serum negative. Additionally, eight DENV 2 strains in the same serotype were successfully differentiated by an HRM assay. Thus, this assay facilitated accurate detection and serotyping of DENV, along with the time-sensitive identification of the infectious focus of different DENVs.

Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China.

BACKGROUND: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis. METHODS: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020. RESULTS: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions. CONCLUSIONS: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB.

Polyphyllin I alleviates lipopolysaccharide-induced inflammation reduces pyroptosis in BEAS-2B and HPAEC cells by inhibiting NF-κB signaling.

Polyphyllin I is an active steroidal saponin isolated from Paris polyphylla with anti-cancer and anti-inflammatory properties. The present study investigates the role of polyphyllin I in acute lung injury. Firstly, the human bronchial epithelial cells (BEAS-2B) and human pulmonary artery endothelial cells (HPAEC) were stimulated with increasing concentrations of lipopolysaccharide at 2, 5, and 10 µg/mL. The treatment with lipopolysaccharide reduced the cell viabilities of BEAS-2B and HPAEC, downregulated superoxide dismutase (SOD) and glutathione (GSH), and up-regulated myeloperoxidase (MPO) and malondialdehyde (MDA). Moreover, the levels of TNF-α, IL-1ß, and IL-6 were also up-regulated in lipopolysaccharide-treated BEAS-2B/HPAEC cells. Secondly, the lipopolysaccharide-treated cells were then incubated with different concentrations of polyphyllin I. Incubation with polyphyllin I enhanced the cell viabilities of lipopolysaccharide--treated BEAS-2B/HPAEC, up-regulated levels of SOD and GSH, and reduced MPO and MDA. Moreover, polyphyllin I reduced TNF-α, IL-1ß, and IL-6 in lipopolysaccharide-treated BEAS-2B/HPAEC cells. Thirdly, the up-regulation of GSDMD-N, pro-caspase-1, and cleaved caspase-1 proteins in lipopolysaccharide-treated BEAS-2B/HPAEC cells were decreased by polyphyllin I. Polyphyllin I increased the protein expression of GSDMD-D in the lipopolysaccharide-treated BEAS-2B/HPAEC cells, and inhibited the translocation of GSDMD from cytoplasm to plasma membrane. Lastly, polyphyllin I reduced the expression of p-p65 in lipopolysaccharide-treated BEAS-2B/HPAEC cells. The over-expression of p65 counteracted with the inhibitory effects of polyphyllin I on oxidative stress and inflammation in lipopolysaccharide-treated BEAS-2B. In conclusion, polyphyllin I repressed the lipopolysaccharide-induced oxidative stress and inflammation in BEAS-2B and HPAEC, and reduced pyroptosis through inhibition of NF-κB signaling.

Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics.

BACKGROUND: Viruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. RESULTS: We present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of k-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28-34%, genus level). PHP also outperformed these two alignment-free methods much (24-38% vs 18-20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP. CONCLUSIONS: The Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly identified prokaryotic viruses in metagenomic studies.