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A bibliometric analysis of m6A methylation in viral infection from 2000 to 2022 - PubMed

  • ️Mon Jan 01 2024

doi: 10.1186/s12985-024-02294-1.

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A bibliometric analysis of m6A methylation in viral infection from 2000 to 2022

Xing Tao et al. Virol J. 2024.

Abstract

Background: N6-methyladenosine (m6A) methylation has become an active research area in viral infection, while little bibliometric analysis has been performed. In this study, we aim to visualize hotspots and trends using bibliometric analysis to provide a comprehensive and objective overview of the current research dynamics in this field.

Methods: The data related to m6A methylation in viral infection were obtained through the Web of Science Core Collection form 2000 to 2022. To reduce bias, the literature search was conducted on December 1, 2022. Bibliometric and visual analyzes were performed using CiteSpace and Bibliometrix package. After screening, 319 qualified records were retrieved.

Results: These publications mainly came from 28 countries led by China and the United States (the US), with the US ranking highest in terms of total link strength.The most common keywords were m6A, COVID-19, epitranscriptomics, METTL3, hepatitis B virus, innate immunity and human immunodeficiency virus 1. The thematic map showed that METTL3, plant viruses, cancer progression and type I interferon (IFN-I) reflected a good development trend and might become a research hotspot in the future, while post-transcriptional modification, as an emerging or declining theme, might not develop well.

Conclusions: In conclusion, m6A methylation in viral infection is an increasingly important topic in articles. METTL3, plant viruses, cancer progression and IFN-I may still be research hotspots and trends in the future.

Keywords: Bibliometric analysis; Data visualization; Methylation; N6-methyladenosine; Viral infection.

© 2024. The Author(s).

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Conflict of interest statement

There are no conflicts of interest among the authors.

Figures

Fig. 1
Fig. 1

The trend of annual publications related to m6A in viral infection

Fig. 2
Fig. 2

Geographical distribution of article publications related to m6A in viral infection and the map of collaboration across countries. A Geographical distribution of article publications. The label infers to the country and the number of articles published by this country, and the depth of color matches the volume of the publications. B The trend of annual publications of the top 10 countries related to m6A in viral infection. The Spain, South Korea and Japan contribute the same volume of the publications. C Visualization map of collaboration across countries. Red links represent partnerships between the two regions. The depth of color matches the number of published articles, gray areas indicate regions with no output

Fig. 3
Fig. 3

Visualization maps of keyword co-occurrence and cluster analysis networks. A Keyword co-occurrence analysis. Each node with colorful annual rings represents a keyword. The size of the nodes matches the publications outputs. The separate areas made up of nodes and links represent the relationship of different keyword. B Keyword cluster analysis. All keywords are divided into 15 clusters. The nodes and edges of different colors represent different clusters. C Visualization timeline view of keywords clustering analysis related to m6A methylation in viral infection. Horizontal lines of different colors with labels represent clusters formed by keywords, nodes on the horizontal line represent keywords, and the positions of nodes on the horizontal line represent the year when documents containing keywords first appeared, thus forming the timeline of the keyword clusters evolution

Fig. 4
Fig. 4

The word cloud and Sankey diagram of author’s keywords related to m6A in viral infection. A The author’s keywords of m6A in viral infection. Colors represent different keywords, and the size represents the frequency of keywords. The words with the highest frequency were m6A, followed by COVID-19, epitranscriptomics, METTL3, hepatitis B virus, innate immunity and HIV-1. B Sankey diagram of the association among keywords, authors and countries

Fig. 5
Fig. 5

Authors collaboration analysis. A Visualization map of the top 7 author collaborations related to m6A methylation in viral infection. Each node with colorful annual rings represents an author. The size of the nodes matches the publications of the author. The nodes circled in purple represent greater centrality. The separate areas made up of nodes and links represent the author collaborative relationships. B Co-occurrence network analysis of institutions reveals collaborative relationships. The map, with a network density of 0.0232, consists of 236 nodes and 642 links. The largest node is the Chinese Academy of Sciences, circled in purple, with a centrality of 0.12, indicating that it plays a critical role in this field. Most institutions have collaborated, such as Duke University, Zhejiang University, University of California San Diego, and The University of Chicago (left). To explore the research topics between institutions, keywords were clustered using the log-likelihood ratio test (LLR), with ‘m6A’ as the keyword being the largest cluster (right)

Fig. 6
Fig. 6

The dual-map overlay of journals related to m6A methylation in viral infection. Nodes on the left represent included documents, and nodes on the right represent references in the documents. Labels represent disciplines, and links represent the cited path

Fig. 7
Fig. 7

Theme evolution and thematic map. A Keyword evolution analysis of m6A methylation and viral infection in different time periods from 2000 to 2020. The nodes represent the main research topics generated from the co-occurrence network analysis, and the number of keywords contained in each node is represented by the size of the corresponding node. Time slices from adjacent segments sharing the same keyword are connected by streamlines, the width of which is proportional to the number of keywords. B Conceptual structure map. The area marked in red indicates the first largest category of keyword clusters, and the blue area indicates a small category. Each dot represents a keyword, and its distance means how often they appear in the article. The proximity of a keyword to the center point represents its popularity in the research field. C Thematic map. The horizontal axis represents centrality, and the vertical axis represents density. The first quadrant (upper right) is motor themes, implying both important and well developed. The second quadrant (upper left) is highly developed and isolated themes, indicating that there has been good development, but not important for the current research field. The third quadrant (lower left) is emerging or declining themes, indicating marginal themes that may not have a good development. The fourth quadrant (lower right) is basic and transversal themes, which are important to the field but have not been well developed (generally basic concepts)

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