TY - GEN
T1 - Fast graph approaches to measure influenza transmission across geographically distributed host types
AU - Breland, Adrienne
AU - Schlauch, Karen
AU - Gunes, Mehmet
AU - Harris, Frederick C.
PY - 2010
Y1 - 2010
N2 - Recent advances in next generation sequencing are provid- ing 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 wholegenome datasets. In this report, our dataset contains over 4; 200 globally sampled Inuenza 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 reect notable amounts of inter-host and inter-regional transmission of Inuenza A viru
AB - Recent advances in next generation sequencing are provid- ing 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 wholegenome datasets. In this report, our dataset contains over 4; 200 globally sampled Inuenza 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 reect notable amounts of inter-host and inter-regional transmission of Inuenza A viru
KW - Measurement
UR - http://www.scopus.com/inward/record.url?scp=77958072844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77958072844&partnerID=8YFLogxK
U2 - 10.1145/1854776.1854887
DO - 10.1145/1854776.1854887
M3 - Conference contribution
AN - SCOPUS:77958072844
SN - 9781450304382
T3 - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
SP - 594
EP - 601
BT - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
T2 - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Y2 - 2 August 2010 through 4 August 2010
ER -