Diagnosing malignant skin tumors accurately at an early stage can be challenging due to ambiguous and even confusing visual characteristics displayed by various categories of skin tumors. To improve diagnosis precision, all available clinical data from multiple sources, particularly clinical images, dermoscopy images, and medical history, could be considered. Aligning with clinical practice, we propose a novel Transformer model, named RemixFormer++ that consists of a clinical image branch, a dermoscopy image branch, and a metadata branch. Given the unique characteristics inherent in clinical and dermoscopy images, specialized attention strategies are adopted for each type. Clinical images are processed through a top-down architecture, capturing both localized lesion details and global contextual information. Conversely, dermoscopy images undergo a bottom-up processing with two-level hierarchical encoders, designed to pinpoint fine-grained structural and textural features. A dedicated metadata branch seamlessly integrates non-visual information by encoding relevant patient data. Fusing features from three branches substantially boosts disease classification accuracy. RemixFormer++ demonstrates exceptional performance on four single-modality datasets (PAD-UFES-20, ISIC 2017/2018/2019). Compared with the previous best method using a public multi-modal Derm7pt dataset, we achieved an absolute 5.3% increase in averaged F1 and 1.2% in accuracy for the classification of five skin tumors. Furthermore, using a large-scale in-house dataset of 10,351 patients with the twelve most common skin tumors, our method obtained an overall classification accuracy of 92.6%. These promising results, on par or better with the performance of 191 dermatologists through a comprehensive reader study, evidently imply the potential clinical usability of our method.

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.

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: 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].