Mixing patterns in a global influenza A virus network using whole genome comparisons

Adrienne E. Breland; Mehmet H. Gunes; Karen A. Schlauch; Frederick C. Harris

doi:10.1109/CIBCB.2010.5510336

Mixing patterns in a global influenza A virus network using whole genome comparisons

Adrienne E. Breland
, Mehmet H. Gunes
, Karen A. Schlauch
, Frederick C. Harris

University of Nevada, Reno

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

Original language	English
Title of host publication	2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
Pages	190-197
Number of pages	8
DOIs	https://doi.org/10.1109/CIBCB.2010.5510336
State	Published - 2010
Event	2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 - Montreal, QC, Canada Duration: 2 May 2010 → 5 May 2010

Publication series

Name	2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010

Conference

Conference	2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
Country/Territory	Canada
City	Montreal, QC
Period	2/05/10 → 5/05/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/CIBCB.2010.5510336

Cite this

Breland, A. E., Gunes, M. H., Schlauch, K. A., & Harris, F. C. (2010). Mixing patterns in a global influenza A virus network using whole genome comparisons. In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 (pp. 190-197). Article 5510336 (2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010). https://doi.org/10.1109/CIBCB.2010.5510336

Breland, Adrienne E. ; Gunes, Mehmet H. ; Schlauch, Karen A. et al. / Mixing patterns in a global influenza A virus network using whole genome comparisons. 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010. 2010. pp. 190-197 (2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010).

@inproceedings{ba650720f5b949f18f13596dd7fb40d3,

title = "Mixing patterns in a global influenza A virus network using whole genome comparisons",

abstract = "Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.",

author = "Breland, \{Adrienne E.\} and Gunes, \{Mehmet H.\} and Schlauch, \{Karen A.\} and Harris, \{Frederick C.\}",

year = "2010",

doi = "10.1109/CIBCB.2010.5510336",

language = "English",

isbn = "9781424467662",

series = "2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010",

pages = "190--197",

booktitle = "2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010",

note = "2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 ; Conference date: 02-05-2010 Through 05-05-2010",

}

Breland, AE, Gunes, MH, Schlauch, KA & Harris, FC 2010, Mixing patterns in a global influenza A virus network using whole genome comparisons. in 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010., 5510336, 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010, pp. 190-197, 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010, Montreal, QC, Canada, 2/05/10. https://doi.org/10.1109/CIBCB.2010.5510336

Mixing patterns in a global influenza A virus network using whole genome comparisons. / Breland, Adrienne E.; Gunes, Mehmet H.; Schlauch, Karen A. et al.
2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010. 2010. p. 190-197 5510336 (2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mixing patterns in a global influenza A virus network using whole genome comparisons

AU - Breland, Adrienne E.

AU - Gunes, Mehmet H.

AU - Schlauch, Karen A.

AU - Harris, Frederick C.

PY - 2010

Y1 - 2010

N2 - Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

AB - Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

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Breland AE, Gunes MH, Schlauch KA, Harris FC. Mixing patterns in a global influenza A virus network using whole genome comparisons. In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010. 2010. p. 190-197. 5510336. (2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010). doi: 10.1109/CIBCB.2010.5510336

Mixing patterns in a global influenza A virus network using whole genome comparisons

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