| Title |
Phylogenetic comparison of Epstein-Barr virus genomes
|
|---|---|
| Published in |
Journal of Microbiology, June 2018
|
| DOI | 10.1007/s12275-018-8039-x |
| Pubmed ID | |
| Authors |
Su Jin Choi, Seok Won Jung, Sora Huh, Hyosun Cho, Hyojeung Kang |
| Abstract |
Technologies used for genome analysis and whole genome sequencing are useful for us to understand genomic characterization and divergence. The Epstein-Barr virus (EBV) is an oncogenic virus that causes diverse diseases such as Burkitt's lymphoma (BL), nasopharyngeal carcinoma (NPC), Hodgkin's lymphoma (HL), and gastric carcinoma (GC). EBV genomes found in these diseases can be classified either by phases of EBV latency (type-I, -II, and -III latency) or types of EBNA2 sequence difference (type-I EBV, type-II EBV or EBV-1, EBV-2). EBV from EBV-transformed lymphoblastoid cell line (LCL) establishes type-III latency, EBV from NPC establishes type-II latency, and EBV from GC establishes type-I latency. However, other important factors play key roles in classifying numerous EBV strains because EBV genomes are highly diverse and not phylogenetically related to types of EBV-associated diseases. Herein, we first reviewed previous studies to describe molecular characteristics of EBV genomes. Then, using comparative and phylogenetic analyses, we phylogenetically analyzed molecular variations of EBV genomes and proteins. The review of previous studies and our phylogenetic analysis showed that EBV genomes and proteins were highly diverse regardless of types of EBV-associated diseases. Other factors should be considered in determining EBV taxonomy. This review will be helpful to understand complicated phylogenetic relationships of EBV genomes. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 26% |
| Student > Master | 6 | 13% |
| Student > Doctoral Student | 5 | 11% |
| Student > Ph. D. Student | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Other | 4 | 9% |
| Unknown | 10 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 28% |
| Medicine and Dentistry | 9 | 20% |
| Immunology and Microbiology | 7 | 15% |
| Agricultural and Biological Sciences | 3 | 7% |
| Physics and Astronomy | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 13 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2018.
All research outputs
#21,476,880
of 23,975,976 outputs
Outputs from Journal of Microbiology
#677
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Outputs of similar age
#292,282
of 332,149 outputs
Outputs of similar age from Journal of Microbiology
#18
of 25 outputs
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