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Fast graph approaches to measure influenza transmission across geographically distributed host types

Published: 02 August 2010 Publication History

Abstract

Recent advances in next generation sequencing are providing a number of large whole-genome sequence datasets stemming from globally distributed disease occurrences. This offers an unprecedented opportunity for epidemiological studies and the development of computationally efficient, robust tools for such studies. Here we present an analytic approach combining several existing tools that enables a quick, effective, and robust epidemiological analysis of large whole-genome datasets. In this report, our dataset contains over 4,200 globally sampled Influenza A virus isolates from multiple host type, subtypes, and years. These sequences are compared using an alignment-free method that runs in linear time. This enables us to generate a disease transmission network where sequences serve as nodes, and high-degree sequence similarity as edges. Mixing patterns are then used to examine statistical probabilities of edge formation among different host types from different global regions and from different localities within Southeast Asia. Our results reflect notable amounts of inter-host and inter-regional transmission of Influenza A virus.

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  • (2014)Complex Network Analysis of Research Funding: A Case Study of NSF GrantsState of the Art Applications of Social Network Analysis10.1007/978-3-319-05912-9_8(163-187)Online publication date: 15-May-2014

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    cover image ACM Conferences
    BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
    August 2010
    705 pages
    ISBN:9781450304382
    DOI:10.1145/1854776
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    Published: 02 August 2010

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    • (2014)Complex Network Analysis of Research Funding: A Case Study of NSF GrantsState of the Art Applications of Social Network Analysis10.1007/978-3-319-05912-9_8(163-187)Online publication date: 15-May-2014

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