scholarly journals An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

2022 ◽  
Author(s):  
Yunchao Ling ◽  
Ruifang Cao ◽  
Jiaqiang Qian ◽  
Jiefu Li ◽  
Haokui Zhou ◽  
...  
2020 ◽  
Author(s):  
Yunchao Ling ◽  
Ruifang Cao ◽  
Jiaqiang Qian ◽  
Jiefu Li ◽  
Haokui Zhou ◽  
...  

AbstractComprehensive analyses of viral genomes can provide a global picture on SARS-CoV-2 transmission and help to predict the oncoming trends of pandemic. This molecular tracing is mainly conducted through extensive phylogenetic network analyses. However, the rapid accumulation of SARS-CoV-2 genomes presents an unprecedented data size and complexity that has exceeded the capacity of existing methods in constructing evolution network through virus genotyping. Here we report a Viral genome Evolution Network Analysis System (VENAS), which uses Hamming distances adjusted by the minor allele frequency to construct viral genome evolution network. The resulting network was topologically clustered and divided using community detection algorithm, and potential evolution paths were further inferred with a network disassortativity trimming algorithm. We also employed parallel computing technology to achieve rapid processing and interactive visualization of >10,000 viral genomes, enabling accurate detection and subtyping of the viral mutations through different stages of Covid-19 pandemic. In particular, several core viral mutations can be independently identified and linked to early transmission events in Covid-19 pandemic. As a general platform for comprehensive viral genome analysis, VENAS serves as a useful computational tool in the current and future pandemics.


Nature ◽  
2005 ◽  
Vol 437 (7062) ◽  
pp. 1162-1166 ◽  
Author(s):  
Elodie Ghedin ◽  
Naomi A. Sengamalay ◽  
Martin Shumway ◽  
Jennifer Zaborsky ◽  
Tamara Feldblyum ◽  
...  

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  

2018 ◽  
Vol 92 (22) ◽  
Author(s):  
Tomofumi Mochizuki ◽  
Rie Ohara ◽  
Marilyn J. Roossinck

ABSTRACTThe effect of large-scale synonymous substitutions in a small icosahedral, single-stranded RNA viral genome on virulence, viral titer, and protein evolution were analyzed. The coat protein (CP) gene of the Fny stain of cucumber mosaic virus (CMV) was modified. We created four CP mutants in which all the codons of nine amino acids in the 5′ or 3′ half of the CP gene were replaced by either the most frequently or the least frequently used synonymous codons in monocot plants. When the dicot host (Nicotiana benthamiana) was inoculated with these four CP mutants, viral RNA titers in uninoculated symptomatic leaves decreased, while all mutants eventually showed mosaic symptoms similar to those for the wild type. The codon adaptation index of these four CP mutants against dicot genes was similar to those of the wild-type CP gene, indicating that the reduction of viral RNA titer was due to deleterious changes of the secondary structure of RNAs 3 and 4. When two 5′ mutants were serially passaged inN. benthamiana, viral RNA titers were rapidly restored but competitive fitness remained decreased. Although no nucleic acid changes were observed in the passaged wild-type CMV, one to three amino acid changes were observed in the synonymously mutated CP of each passaged virus, which were involved in recovery of viral RNA titer of 5′ mutants. Thus, we demonstrated that deleterious effects of the large-scale synonymous substitutions in the RNA viral genome facilitated the rapid amino acid mutation(s) in the CP to restore the viral RNA titer.IMPORTANCERecently, it has been known that synonymous substitutions in RNA virus genes affect viral pathogenicity and competitive fitness by alteration of global or local RNA secondary structure of the viral genome. We confirmed that large-scale synonymous substitutions in the CP gene of CMV resulted in decreased viral RNA titer. Importantly, when viral evolution was stimulated by serial-passage inoculation, viral RNA titer was rapidly restored, concurrent with a few amino acid changes in the CP. This novel finding indicates that the deleterious effects of large-scale nucleic acid mutations on viral RNA secondary structure are readily tolerated by structural changes in the CP, demonstrating a novel part of the adaptive evolution of an RNA viral genome. In addition, our experimental system for serial inoculation of large-scale synonymous mutants could uncover a role for new amino acid residues in the viral protein that have not been observed in the wild-type virus strains.


PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146220 ◽  
Author(s):  
Aleksandra do Socorro da Silva ◽  
Silvana Rossy de Brito ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
Cláudio Alex Jorge da Rocha ◽  
Maurílio de Abreu Monteiro ◽  
...  

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Apurva Badkas ◽  
Thanh-Phuong Nguyen ◽  
Laura Caberlotto ◽  
Jochen G. Schneider ◽  
Sébastien De Landtsheer ◽  
...  

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.


2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.


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