Virus long noncoding RNAs (vlncRNAs) play crucial roles in viral infections, yet their identification and characterization remain limited. This study identified 5,053 novel vlncRNAs across 25 viral species using third-generation sequencing, with two from Influenza A virus and Vesicular stomatitis virus validated by RT-qPCR. Most vlncRNAs originated from dsDNA viruses. Only ~1% of vlncRNAs have annotated RNA families, suggesting many novel RNA structures. Interestingly, a total of 772 vlncRNAs from 15 human viruses structurally mimicked human lncRNAs (hlncRNAs), while only seven vlncRNAs shared sequence similarities with hlncRNAs. These vlncRNA and hlncRNAs bound to similar miRNAs, potentially acting as miRNA sponges to promote essential life processes. Splicing analysis showed vlncRNAs had a prevalence of alternative first exon. Finally, we developed vlncRNAbase (http://computationalbiology.cn/vlncRNAbase/#/) to store and organize the newly identified and known vlncRNAs. Overall, the study provides a valuable resource for further investigation into vlncRNAs and deepens our understanding of the diversity, structure, and function of the molecule.

Influenza poses a significant global public health threat, with vaccination being the most effective and economical preventive measure. However, these punctuated antigenic changes, particularly in HA, result in escape from the immunity that was induced by prior infection or vaccination. Accurately predicting antigenic variation and understanding the antigenic dynamics of influenza viruses are crucial for selecting appropriate vaccine strains, but no established methods exist for influenza B viruses. Therefore, we present PREDAC-FluB, a hybrid deep learning framework that integrates spatial feature extraction via CNN to model interactions in HA1 sequences, multimodal sequence representation combining ESM-2 embeddings with six physicochemical descriptors and continuous encoding (ESM2-7-features), and UMAP-guided clustering for antigenic cluster identification. Using data from 9036 B/Victoria-lineage and 4520 B/Yamagata-lineage influenza virus pair. PREDAC-FluB demonstrates superior performance over traditional machine learning methods in predicting antigenic variation in influenza viruses, successfully identifying major antigenic clusters. Specifically, PREDAC-FluB classified the B/Victoria lineage into nine antigenic clusters and the B/Yamagata lineage into three antigenic clusters. In five-fold cross-validation for B/Victoria viruses, PREDAC-FluB with ESM2-7-features encoding achieved AUROC values of 0.9961 on the validation set and 0.9856 on the independent test set. In retrospective testing for B/Victoria viruses, PREDAC-FluB achieved AUROC values ranging from 0.83 to 0.97, demonstrating high prediction accuracy and effectively capturing antigenic variation information. In conclusion, PREDAC-FluB is a robust tool for antigenic computation, capable of accurately predicting antigenic variation in influenza B viruses. Its high prediction accuracy makes it a promising auxiliary method for recommending future influenza vaccine strains.

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.

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.

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.

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.

Circular RNAs (circRNAs) are covalently closed long noncoding RNAs critical in diverse cellular activities and multiple human diseases. Several cancer-related viral circRNAs have been identified in double-stranded DNA viruses (dsDNA), yet no systematic study about the viral circRNAs has been reported. Herein, we have performed a systematic survey of 11 924 circRNAs from 23 viral species by computational prediction of viral circRNAs from viral-infection-related RNA sequencing data. Besides the dsDNA viruses, our study has also revealed lots of circRNAs in single-stranded RNA viruses and retro-transcribing viruses, such as the Zika virus, the Influenza A virus, the Zaire ebolavirus, and the Human immunodeficiency virus 1. Most viral circRNAs had reverse complementary sequences or repeated sequences at the flanking sequences of the back-splice sites. Most viral circRNAs only expressed in a specific cell line or tissue in a specific species. Functional enrichment analysis indicated that the viral circRNAs from dsDNA viruses were involved in KEGG pathways associated with cancer. All viral circRNAs presented in the current study were stored and organized in VirusCircBase, which is freely available at http://www.computationalbiology.cn/ViruscircBase/home.html and is the first virus circRNA database. VirusCircBase forms the fundamental atlas for the further exploration and investigation of viral circRNAs in the context of public health.

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.

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.

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.

African swine fever virus (ASFV) poses serious threats to the pig industry. The multigene family (MGF) proteins are extensively distributed in ASFVs and are generally classified into five families, including MGF-100, MGF-110, MGF-300, MGF-360 and MGF-505. Most MGF proteins, however, have not been well characterized and classified within each family. To bridge this gap, this study first classified MGF proteins into 31 groups based on protein sequence homology and network clustering. A web server for classifying MGF proteins was established and kept available for free at http://www.computationalbiology.cn/MGF/home.html. Results showed that MGF groups of the same family were most similar to each other and had conserved sequence motifs; the genetic diversity of MGF groups varied widely, mainly due to the occurrence of indels. In addition, the MGF proteins were predicted to have large structural and functional diversity, and MGF proteins of the same MGF family tended to have similar structure, location and function. Reconstruction of the ancestral states of MGF groups along the ASFV phylogeny showed that most MGF groups experienced either the copy number variations or the gain-or-loss changes, and most of these changes happened within strains of the same genotype. It is found that the copy number decrease and the loss of MGF groups were much larger than the copy number increase and the gain of MGF groups, respectively, suggesting the ASFV tended to lose MGF proteins in the evolution. Overall, the work provides a detailed classification for MGF proteins and would facilitate further research on MGF proteins.

MOTIVATION: Newly emerging influenza viruses keep challenging global public health. To evaluate the potential risk of the viruses, it is critical to rapidly determine the phenotypes of the viruses, including the antigenicity, host, virulence and drug resistance. RESULTS: Here, we built FluPhenotype, a one-stop platform to rapidly determinate the phenotypes of the influenza A viruses. The input of FluPhenotype is the complete or partial genomic/protein sequences of the influenza A viruses. The output presents five types of information about the viruses: (i) sequence annotation including the gene and protein names as well as the open reading frames, (ii) potential hosts and human-adaptation-associated amino acid markers, (iii) antigenic and genetic relationships with the vaccine strains of different HA subtypes, (iv) mammalian virulence-related amino acid markers and (v) drug resistance-related amino acid markers. FluPhenotype will be a useful bioinformatic tool for surveillance and early warnings of the newly emerging influenza A viruses. AVAILABILITY AND IMPLEMENTATION: It is publicly available from: http://www.computationalbiology.cn : 18888/IVEW. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Receptors on host cells play a critical role in viral infection. How phages select receptors is still unknown. RESULTS: Here, we manually curated a high-quality database named phageReceptor, including 427 pairs of phage-host receptor interactions, 341 unique viral species or sub-species and 69 bacterial species. Sugars and proteins were most widely used by phages as receptors. The receptor usage of phages in Gram-positive bacteria was different from that in Gram-negative bacteria. Most protein receptors were located on the outer membrane. The phage protein receptors (PPRs) were highly diverse in their structures, and had little sequence identity and no common protein domain with mammalian virus receptors. Further functional characterization of PPRs in Escherichia coli showed that they had larger node degrees and betweennesses in the protein-protein interaction network, and higher expression levels, than other outer membrane proteins, plasma membrane proteins or other intracellular proteins. These findings were consistent with what observed for mammalian virus receptors reported in previous studies, suggesting that viral protein receptors tend to have multiple interaction partners and high expressions. The study deepens our understanding of virus-host interactions. AVAILABILITY AND IMPLEMENTATION: phageReceptor is publicly available from: http://www.computationalbiology.cn/phageReceptor/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Receptor mediated entry is the first step for viral infection. However, the question of how viruses select receptors remains unanswered. RESULTS: Here, by manually curating a high-quality database of 268 pairs of mammalian virus-host receptor interaction, which included 128 unique viral species or sub-species and 119 virus receptors, we found the viral receptors are structurally and functionally diverse, yet they had several common features when compared to other cell membrane proteins: more protein domains, higher level of N-glycosylation, higher ratio of self-interaction and more interaction partners, and higher expression in most tissues of the host. This study could deepen our understanding of virus-receptor interaction. AVAILABILITY AND IMPLEMENTATION: The database of mammalian virus-host receptor interaction is available at http://www.computationalbiology.cn: 5000/viralReceptor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Previously, we developed a computational model to identify genomic co-occurrence networks that was applied to capture the coevolution patterns within genomes of influenza viruses. To facilitate easy public use of this model, an R package 'cooccurNet' is presented here. RESULTS: 'cooccurNet' includes functionalities of construction and analysis of residues (e.g. nucleotides, amino acids and SNPs) co-occurrence network. In addition, a new method for measuring residues coevolution, defined as residue co-occurrence score (RCOS), is proposed and implemented in 'cooccurNet' based on the co-occurrence network. AVAILABILITY AND IMPLEMENTATION: 'cooccurNet' is publicly available on CRAN repositories under the GPL-3 Open Source License ( http://cran.r-project.org/package=cooccurNet ). CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Timely surveillance of the antigenic dynamics of the influenza virus is critical for accurate selection of vaccine strains, which is important for effective prevention of viral spread and infection. RESULTS: Here, we provide a computational platform, called PREDAC-H3, for antigenic surveillance of human influenza A(H3N2) virus based on the sequence of surface protein hemagglutinin (HA). PREDAC-H3 not only determines the antigenic variants and antigenic cluster (grouped for similar antigenicity) to which the virus belongs, based on HA sequences, but also allows visualization of the spatial distribution and temporal dynamics of antigenic clusters of viruses isolated from around the world, thus assisting in antigenic surveillance of human influenza A(H3N2) virus. AVAILABILITY AND IMPLEMENTATION: It is publicly available from: http://biocloud.hnu.edu.cn/influ411/html/index.php CONTACTS: : [email protected] or [email protected].

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention.