Analysis of viral diversity for vaccine target discovery

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Author(s)
Khan, Asif M
Hu, Yongli
Miotto, Olivo
Thevasagayam, Natascha M
Sukumaran, Rashmi
Abd Raman, Hadia Syahirah
Brusic, Vladimir
Tan, Tin Wee
Thomas August, J
Griffith University Author(s)
Year published
2017
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Show full item recordAbstract
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 ...
View more >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.
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View more >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.
View less >
Journal Title
BMC Medical Genomics
Volume
10(Suppl 4)
Issue
78
Copyright Statement
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Subject
Genetics
Medical biochemistry and metabolomics
Medical biochemistry and metabolomics not elsewhere classified
Oncology and carcinogenesis