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Conference Paper: Application of large-scale sequencing and data analysis to research on emerging infectious diseases

TitleApplication of large-scale sequencing and data analysis to research on emerging infectious diseases
Authors
Issue Date2017
PublisherOxford University Press.
Citation
21st International Bioinformatics Workshop on Virus Evolution and Molecular Epidemiology (VEME), Seoul, Korea, 14-19 August 2016. In Virus Evolution, 2017, v. 3 n. Suppl 1, p. vew036.023 How to Cite?
AbstractMany human diseases are caused by emerging pathogens, such as the SARS and MERS coronaviruses. Timely understanding of the behaviors of these pathogens plays an important role in helping doctors and scientists in searching for treatment methods and designing vaccines. The development of next-generation sequencing (NGS) has led to significant breakthroughs in the production of large amount of unbiased DNA sequence data from field and human clinical samples, providing the capacity to identify the sources of infection, and the virus evolution as well as host/virus interaction. In our study, using 454/Illumina sequencing, we have obtained large amount of whole genome sequences. We designed a preliminary bioinformatics analysis pipeline to classify these NGS reads. First we mapped our nucleotide reads to GenBank reference sequences using BLAST, and classified them by their taxonomic family, such as host, virus and unclassified. Then, for a specific type of virus (e.g. influenza virus, MERS coronavirus), we conducted de novo and reference based assembly of the reads to obtain the full genome sequences for further phylogenetic study. In the future, through advanced bioinformatics tools, we hope to get more detailed information from our large amount of NGS sequences of field/clinical samples, experimental data, especially in the following areas: (i) finding novel pathogens in unclassified sequences; (ii) virus/virus interactions; (iii) pathogen/host interaction.
Persistent Identifierhttp://hdl.handle.net/10722/247081
ISSN
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Y-
dc.contributor.authorLam, TY-
dc.contributor.authorZhu, H-
dc.contributor.authorGuan, Y-
dc.date.accessioned2017-10-18T08:21:56Z-
dc.date.available2017-10-18T08:21:56Z-
dc.date.issued2017-
dc.identifier.citation21st International Bioinformatics Workshop on Virus Evolution and Molecular Epidemiology (VEME), Seoul, Korea, 14-19 August 2016. In Virus Evolution, 2017, v. 3 n. Suppl 1, p. vew036.023-
dc.identifier.issn2057-1577-
dc.identifier.urihttp://hdl.handle.net/10722/247081-
dc.description.abstractMany human diseases are caused by emerging pathogens, such as the SARS and MERS coronaviruses. Timely understanding of the behaviors of these pathogens plays an important role in helping doctors and scientists in searching for treatment methods and designing vaccines. The development of next-generation sequencing (NGS) has led to significant breakthroughs in the production of large amount of unbiased DNA sequence data from field and human clinical samples, providing the capacity to identify the sources of infection, and the virus evolution as well as host/virus interaction. In our study, using 454/Illumina sequencing, we have obtained large amount of whole genome sequences. We designed a preliminary bioinformatics analysis pipeline to classify these NGS reads. First we mapped our nucleotide reads to GenBank reference sequences using BLAST, and classified them by their taxonomic family, such as host, virus and unclassified. Then, for a specific type of virus (e.g. influenza virus, MERS coronavirus), we conducted de novo and reference based assembly of the reads to obtain the full genome sequences for further phylogenetic study. In the future, through advanced bioinformatics tools, we hope to get more detailed information from our large amount of NGS sequences of field/clinical samples, experimental data, especially in the following areas: (i) finding novel pathogens in unclassified sequences; (ii) virus/virus interactions; (iii) pathogen/host interaction.-
dc.languageeng-
dc.publisherOxford University Press.-
dc.relation.ispartofVirus Evolution-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here].-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleApplication of large-scale sequencing and data analysis to research on emerging infectious diseases-
dc.typeConference_Paper-
dc.identifier.emailLiu, Y: yongmei@hkucc.hku.hk-
dc.identifier.emailLam, TY: ttylam@hku.hk-
dc.identifier.emailZhu, H: zhuhch@hku.hk-
dc.identifier.emailGuan, Y: yguan@hkucc.hku.hk-
dc.identifier.authorityLam, TY=rp01733-
dc.identifier.authorityZhu, H=rp01535-
dc.identifier.authorityGuan, Y=rp00397-
dc.description.naturepublished_or_final_version-
dc.identifier.pmcidPMC5565992-
dc.identifier.hkuros280542-
dc.identifier.volume3-
dc.identifier.issueSuppl 1-
dc.identifier.spagevew036.023-
dc.identifier.epagevew036.023-
dc.publisher.placeOxford, UK-

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