Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking.

The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.

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.

covSampler: A subsampling method with balanced genetic diversity for large-scale SARS-CoV-2 genome data sets.

Phylogenetic analysis has been widely used to describe, display, and infer the evolutionary patterns of viruses. The unprecedented accumulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to post-subsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users' requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

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.

Genomic evidence for divergent co-infections of co-circulating SARS-CoV-2 lineages.

Co-infection of RNA viruses may contribute to their recombination and cause severe clinical symptoms. However, the tracking and identification of SARS-CoV-2 co-infection persist as challenges. Due to the lack of methods for detecting co-infected samples in a large amount of deep sequencing data, the lineage composition, spatial-temporal distribution, and frequency of SARS-CoV-2 co-infection events in the population remains unclear. Here, we propose a hypergeometric distribution-based method named Cov2Coinfect with the ability to decode the lineage composition from 50,809 deep sequencing data. By resolving the mutational patterns in each sample, Cov2Coinfect can precisely determine the co-infected SARS-CoV-2 variants from deep sequencing data. Results from two independent and parallel projects in the United States achieved a similar co-infection rate of 0.3-0.5 % in SARS-CoV-2 positive samples. Notably, all co-infected variants were highly consistent with the co-circulating SARS-CoV-2 lineages in the regional epidemiology, demonstrating that the co-circulation of different variants is an essential prerequisite for co-infection. Overall, our study not only provides a robust method to identify the co-infected SARS-CoV-2 variants from sequencing samples, but also highlights the urgent need to pay more attention to co-infected patients for better disease prevention and control.

Detecting Potentially Adaptive Mutations from the Parallel and Fixed Patterns in SARS-CoV-2 Evolution.

Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention.

Genetic differentiation and diversity of SARS-CoV-2 Omicron variant in its early outbreak.

The recently emerged Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has quickly spread around the world. Although many consensus mutations of the Omicron variant have been recognized, little is known about its genetic variation during its transmission in the population. Here, we comprehensively analyzed the genetic differentiation and diversity of the Omicron variant during its early outbreak. We found that Omicron achieved more structural variations, especially deletions, on the SARS-CoV-2 genome than the other four variants of concern (Alpha, Beta, Gamma, and Delta) in the same timescale. In addition, the Omicron variant acquired, except for 50 consensus mutations, seven great new non-synonymous nucleotide substitutions during its spread. Three of them are on the S protein, including S_A701V, S_L1081V, and S_R346K, which belong to the receptor-binding domain (RBD). The Omicron BA.1 branch could be divided into five divergent groups spreading across different countries and regions based on these seven novel mutations. Furthermore, we found that the Omicron variant possesses more mutations related to a faster transmission rate than the other SARS-CoV-2 variants by assessing the relationship between the genetic diversity and transmission rate. The findings indicated that more attention should be paid to the significant genetic differentiation and diversity of the Omicron variant for better disease prevention and control.

Conserved Pattern and Potential Role of Recurrent Deletions in SARS-CoV-2 Evolution.

SARS-CoV-2 continues adapting to human hosts during the current worldwide pandemic since 2019. This virus evolves through multiple means, such as single nucleotide mutations and structural variations, which has brought great difficulty to disease prevention and control of COVID-19. Structural variation, including multiple nucleotide changes like insertions and deletions, has a greater impact relative to single nucleotide mutation on both genome structures and protein functions. In this study, we found that deletion occurred frequently in not only SARS-CoV-2 but also in other SARS-related coronaviruses. These deletions showed obvious location bias and formed 45 recurrent deletion regions in the viral genome. Some of these deletions showed proliferation advantages, including four high-frequency deletions (nsp6 Δ106-109, S Δ69-70, S Δ144, and Δ28271) that were detected in around 50% of SARS-CoV-2 genomes and other 19 median-frequency deletions. In addition, the association between deletions and the WHO reported variants of concern (VOC) and variants of interest (VOI) of SARS-CoV-2 indicated that these variants had a unique combination of deletion patterns. In the spike (S) protein, the deletions in SARS-CoV-2 were mainly in the N-terminal domain. Some deletions, such as S Δ144/145 and S Δ243-244, have been confirmed to block the binding sites of neutralizing antibodies. Overall, this study revealed a conservative regional pattern and the potential effect of some deletions in SARS-CoV-2 over the whole genome, providing important evidence for potential epidemic control and vaccine development. IMPORTANCE Mutations in SARS-CoV-2 were studied extensively, while only the structure variations on the spike protein were discussed well in previous studies. To study the role of structural variations in virus evolution, we described the distribution of structure variations on the whole genome. Conserved patterns were found of deletions among SARS-CoV-2, SARS-CoV-2-like, and SARS-CoV-like viruses. There were 45 recurrent deletion regions (RDRs) in SARS-CoV-2 generated through the integration of deleted positions. In these regions, four high-frequency deletions parallelly appeared in multiple strains. Furthermore, in the spike protein, the deletions in SARS-CoV-2 were mainly in the N-terminal domain, blocking the binding sites of some neutralizing antibodies, while the structural variations in SARS-related coronavirus were mainly in the N-terminal domain and receptor binding domain. The receptor binding domain is highly related to hosting recognition. The deletions in the receptor binding domain may play a role in host adaption.

One year of SARS-CoV-2 evolution.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to increase transmission. One year after its emergence, we now further analyze emergent SARS-CoV-2 genome sequences in an effort to understand the evolution of this virus.

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.

Mutations, Recombination and Insertion in the Evolution of 2019-nCoV.

Background: The 2019 novel coronavirus (2019-nCoV or SARS-CoV-2) has spread more rapidly than any other betacoronavirus including SARS-CoV and MERS-CoV. However, the mechanisms responsible for infection and molecular evolution of this virus remained unclear. Methods: We collected and analyzed 120 genomic sequences of 2019-nCoV including 11 novel genomes from patients in China. Through comprehensive analysis of the available genome sequences of 2019-nCoV strains, we have tracked multiple inheritable SNPs and determined the evolution of 2019-nCoV relative to other coronaviruses. Results: Systematic analysis of 120 genomic sequences of 2019-nCoV revealed co-circulation of two genetic subgroups with distinct SNPs markers, which can be used to trace the 2019-nCoV spreading pathways to different regions and countries. Although 2019-nCoV, human and bat SARS-CoV share high homologous in overall genome structures, they evolved into two distinct groups with different receptor entry specificities through potential recombination in the receptor binding regions. In addition, 2019-nCoV has a unique four amino acid insertion between S1 and S2 domains of the spike protein, which created a potential furin or TMPRSS2 cleavage site. Conclusions: Our studies provided comprehensive insights into the evolution and spread of the 2019-nCoV. Our results provided evidence suggesting that 2019-nCoV may increase its infectivity through the receptor binding domain recombination and a cleavage site insertion. One Sentence Summary: Novel 2019-nCoV sequences revealed the evolution and specificity of betacoronavirus with possible mechanisms of enhanced infectivity.

Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes.

MOTIVATION: Many methods have been developed to estimate immune cell composition from tissue transcriptomes. One common characteristic of these methods is that they are trained using a set of general immune cell transcriptomes that ignores tissue specificities. However, as immune cells are localized in different tissues, they may have distinct expression profiles. Hence, calculations that use general signature matrices may hinder the deconvolution accuracy. RESULTS: This study used single cell RNA-sequencing (scRNA-Seq) data from different mouse tissues instead of general signature expression values to generate tissue-specific signature gene matrices that are used as the input of the deconvolution model. First, the transcriptome of immune cells in each tissue was extracted from scRNA-Seq data and used to construct the entire expression matrix of tissue immune cells. Then, after comparing different gene selection strategies, the expressions of 162 seq-ImmuCC derived signature genes in tissue immune cell scRNA-Seq data were regarded as the tissue specific signature matrices. Finally, a modest improvement in performance was observed in multiple tissues that refer to a traditional general signature matrix in the deconvolution model. With the fast accumulation of scRNA-Seq data, the introduction of these data into an estimation of immune cell compositions for different tissues will open a new window for avoiding tissue bias for immune cell expression. AVAILABILITY AND IMPLEMENTATION: The signature matrices were available at https://github.com/wuaipinglab/ImmuCC/tree/master/tissue_immucc/SignatureMatrix). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cluster-Transition Determining Sites Underlying the Antigenic Evolution of Seasonal Influenza Viruses.

Seasonal influenza viruses undergo frequent mutations on their surface hemagglutinin (HA) proteins to escape the host immune response. In these mutations, a few key amino acid sites are associated with significant antigenic cluster transitions. To recognize the cluster-transition determining sites of seasonal influenza A/H3N2 and A/H1N1 viruses systematically and quickly, we developed a computational model named RECDS (recognition of cluster-transition determining sites) to evaluate the contribution of a specific amino acid site on the HA protein in the whole history of antigenic evolution. In RECDS, we ranked all of the HA sites by calculating the contribution scores derived from the forest of gradient boosting classifiers trained by various sequence- and structure-based features. With the RECDS model, we found out that the sites determining influenza antigenicity were mostly around the receptor-binding domain both for the influenza A/H3N2 and A/H1N1 viruses. Specifically, half of the cluster-transition determining sites of the influenza A/H1N1 virus were located in the vestigial esterase domain and basic path area on the HA, which indicated that the differential driving force of the antigenic evolution of the A/H1N1 virus refers to the A/H3N2 virus. Beyond that, the footprints of substitutions responsible for antigenic evolution were inferred according to the phylogenetic trees for the cluster-transition determining sites. The monitoring of genetic variation occurring at these cluster-transition determining sites in circulating influenza viruses on a large scale will potentially reduce current assay workloads in influenza surveillance and the selection of new influenza vaccine strains.