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Title: Analysis of viral diversity for vaccine target discovery
Authors: Asif M. Khan
Yongli Hu
Olivo Miotto
Natascha M. Thevasagayam
Rashmi Sukumaran
Hadia Syahirah Abd Raman
Vladimir Brusic
Tin Wee Tan
J. Thomas August
Perdana University
Johns Hopkins University
Yong Loo Lin School of Medicine
University of Oxford
Mahidol University
Griffith University
Keywords: Biochemistry, Genetics and Molecular Biology
Issue Date: 21-Dec-2017
Citation: BMC Medical Genomics. Vol.10, (2017)
Abstract: © 2017 The Author(s). Background: Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets. Results: This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis. Conclusion: These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.
ISSN: 17558794
Appears in Collections:Scopus 2016-2017

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