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      Host genetics of Epstein–Barr virus infection, latency and disease

      review-article
      1 , 2 , 1 , 3 , *
      Reviews in Medical Virology
      Blackwell Publishing Ltd

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          Abstract

          Epstein–Barr virus (EBV) infects 95% of the adult population and is the cause of infectious mononucleosis. It is also associated with 1% of cancers worldwide, such as nasopharyngeal carcinoma, Hodgkin's lymphoma and Burkitt's lymphoma. Human and cancer genetic studies are now major forces determining gene variants associated with many cancers, including nasopharyngeal carcinoma and Hodgkin's lymphoma. Host genetics is also important in infectious disease; however, there have been no large-scale efforts towards understanding the contribution that human genetic variation plays in primary EBV infection and latency. This review covers 25 years of studies into host genetic susceptibility to EBV infection and disease, from candidate gene studies, to the first genome-wide association study of EBV antibody response, and an EBV-status stratified genome-wide association study of Hodgkin's lymphoma. Although many genes are implicated in EBV-related disease, studies are often small, not replicated or followed up in a different disease. Larger, appropriately powered genomic studies to understand the host response to EBV will be needed to move our understanding of the biology of EBV infection beyond the handful of genes currently identified. Fifty years since the discovery of EBV and its identification as a human oncogenic virus, a glimpse of the future is shown by the first whole-genome and whole-exome studies, revealing new human genes at the heart of the host–EBV interaction. © 2014 The Authors. Reviews in Medical Virology published by John Wiley & Sons Ltd.

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          Most cited references159

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          Circos: an information aesthetic for comparative genomics.

          We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.
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            STRING v9.1: protein-protein interaction networks, with increased coverage and integration

            Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.
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              Epstein-Barr virus infection.

              J I Cohen (2000)
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                Author and article information

                Journal
                Rev Med Virol
                Rev. Med. Virol
                rmv
                Reviews in Medical Virology
                Blackwell Publishing Ltd (Oxford, UK )
                1052-9276
                1099-1654
                March 2015
                27 November 2014
                : 25
                : 2
                : 71-84
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Cambridge, UK
                [2 ]Division of Biological Anthropology, Department of Archaeology and Anthropology, University of Cambridge Cambridge, UK
                [3 ]Department of Infection, Division of Infection and Immunity, University College London London, UK
                Author notes
                *Corresponding author: P. Kellam, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK., E-mail: pk5@ 123456sanger.ac.uk
                Article
                10.1002/rmv.1816
                4407908
                25430668
                3e8fb47f-c451-4bc9-ad40-f9218853dfa4
                © 2014 The Authors Reviews in Medical Virology published by John Wiley & Sons Ltd

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 August 2014
                : 10 October 2014
                : 14 October 2014
                Categories
                Reviews

                Microbiology & Virology
                Microbiology & Virology

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